Ai model unification and access platforms, systems and methods

Abstract

A system may include a prompt pre-processor configured to receive user input and prepare prompts for processing by one or more artificial intelligence models. A system may include a model smart router configured to direct prompts to artificial intelligence models based on prompt characteristics and historical performance data. A system may include a ranking system configured to evaluate responses from multiple artificial intelligence models to determine highest quality outputs. A system may include a configuration system configured to store and manage settings for inline cost management, prompt processing sophistication, and model smart router processing sophistication. A system may include a data and networking system configured to manage data storage and network communications for the platform, wherein the data and networking system includes prompt historical ranked models data storing historical performance information for artificial intelligence models.

Description

Atty Dkt: 11016-8049PCTAl MODEL UNIFICATION AND ACCESS PLATFORMS, SYSTEMS AND METHODSFIELD

[0001] The present disclosure relates to routing user prompts to appropriate Al models. BACKGROUND

[0002] Recent artificial intelligence advances have the potential to greatly enhance productivity for workers in many industries. The workers and industries, however, have not yet adapted to using the recent Al models, such as Large Language Models (LLMs), and uptake remains low despite large investments by businesses to adopt the technology. Many workers claim that existing interfaces are cumbersome, that it is not clear what or how to interact with the technology, that the process is not readily automated, among other difficulties. Additionally, many of the users expect perfection from the Al models and experience errors and hallucinations. A further difficulty of modem Al model use is the large expense involved with use of many popular products. Accordingly, there is a need for improved artificial intelligence systems.SUMMARY

[0003] In embodiments, the techniques described herein relate to Al model unification and routing system.

[0004] In embodiments, a computer-implemented system includes an Al model unification and routing platform. In embodiments, a computer-implemented method includes routing user prompts to suitable Al models. In embodiments, an Al model unification and routing platform substantially as shown and described. In embodiments, a method for unifying and routing Al models substantially as shown and described. In embodiments, a computer-implemented method substantially as hereinbefore described with reference to any of the examples and / or to any of the accompanying drawings. In embodiments, a computing system including one or more processors and one or more memories configured to perform operations substantially as hereinbefore described with reference to any of the examples and / or to any of the accompanying drawings. In embodiments, a computer program product residing on a computer-readable storage medium having a plurality of instructions stored thereon which, when executed across one or more processors, causes at least a portion of the one or more processors to perform operations substantially as hereinbefore described with reference to any of the examples and / or to any of the accompanying drawings. In embodiments, a device configured substantially as hereinbefore described with reference to any of the examples and / or to any of the accompanying drawings. In embodiments, the techniques described herein relate to an artificial intelligence model unification and routing platform including: a prompt pre-processor configured to receive user input and prepare prompts for processing by one or more artificial intelligence models; a model smart router configmed to direct prompts to artificial intelligence models based on prompt characteristics and historical performance data; a ranking system configured to evaluate responses from multiple artificial intelligence models to determine highest quality outputs; a configuration system configured to store and manage settings for inline cost management, prompt processing sophistication, and model smart router processing sophistication; and a data and networking system configmed to manage data storage and network communications for the platform, wherein the data and networking system includes prompt historical ranked models data storing historical performance information for artificial intelligence models.

[0005] In embodiments, the techniques described herein relate to a platform, wherein the prompt pre-processor includes a prompt metadata extractor configured to analyze complexity level of incoming prompts by examining content, context, data elements, questions, and included media.Atty Dkt: 11016-8049PCT

[0006] In embodiments, the techniques described herein relate to a platform, wherein the prompt metadata extractor is configured to generate metadata including keywords, complexity rankings on a scale from 1 to 5, differentiation between data and questions, and temporal nature indicators.

[0007] In embodiments, the techniques described herein relate to a platform, wherein the prompt pre-processor includes a repetition identifier configmed to compare incoming prompts with historical prompt data to identify areas where all or part of a response is provided without invoking artificial intelligence models.

[0008] In embodiments, the techniques described herein relate to a platform, wherein the model smart router includes adaptive learning capabilities configured to improve routing decisions over time through tracking and scoring of artificial intelligence models based on consensus ranking processes.

[0009] In embodiments, the techniques described herein relate to a platform, wherein the model smart router includes a cache for repetitive requests, a local store to optimize context windows, and implements unified model access allowing a single prompt to be issued to multiple artificial intelligence models simultaneously.

[0010] In embodiments, the techniques described herein relate to a platform, wherein the ranking system includes a response challenger configured to perform automated response challenging by generating challenge questions related to a prompt and submitting challenge questions to artificial intelligence models immediately after the models provide initial responses.

[0011] In embodiments, the techniques described herein relate to a platform, wherein the challenge questions are configured to validate answers provided by the models to ensure minimal hallucination and improve accuracy of results.

[0012] In embodiments, the techniques described herein relate to a platform, wherein the ranking system includes a consensus ranker configured to implement a process where multiple artificial intelligence models generate responses to a user-provided prompt, and those responses are evaluated by same or different models which rank each response based on quality, accuracy, clarity, and conciseness.

[0013] In embodiments, the techniques described herein relate to a platform, wherein the configuration system includes inline cost management configured to enable users to establish daily budget limits for costs associated with artificial intelligence model usage, and once a limit has been reached, the system prompts the user and prevents entry of additional prompts unless the user resets the daily budget or provides an override.

[0014] In embodiments, the techniques described herein relate to a method for providing unified access to multiple artificial intelligence models including: receiving, by a prompt pre-processor, user input and preparing prompts for processing by one or more artificial intelligence models; analyzing, by the prompt pre-processor, complexity level of incoming prompts by examining content, context, data elements, questions, and included media; directing, by a model smart router, prompts to appropriate artificial intelligence models based on prompt characteristics, historical performance data, cost considerations, and configuration settings; generating responses from multiple artificial intelligence models; evaluating, by a ranking system, responses from the multiple artificial intelligence models to determine highest quality outputs; storing, by a data and networking system, historical performance information for artificial intelligence models in prompt historical ranked models data; and improving routing decisions over time through tracking and scoring of artificial intelligence models based on consensus ranking processes.

[0015] In embodiments, the techniques described herein relate to a method, further including generating, by the prompt pre-processor, metadata including keywords, complexity rankings, differentiation between data and questions, and temporal nature indicators.Atty Dkt: 11016-8049PCT

[0016] In embodiments, the techniques described herein relate to a method, further including comparing, by the prompt pre-processor, incoming prompts with historical prompt data to identify areas where all or part of a response is provided without invoking artificial intelligence models.

[0017] In embodiments, the techniques described herein relate to a method, further including issuing a single prompt to multiple artificial intelligence models simultaneously and efficiently aggregating responses from the most suitable model.

[0018] In embodiments, the techniques described herein relate to a method, further including performing automated response challenging by generating challenge questions related to a prompt and submitting challenge questions to artificial intelligence models immediately after the models provide initial responses to validate answers and ensure minimal hallucination.

[0019] In embodiments, the techniques described herein relate to a method, further including implementing a consensus ranking process where multiple artificial intelligence models generate responses to a user-provided prompt, and those responses are evaluated by same or different models which rank each response based on quality, accuracy, clarity, and conciseness.

[0020] In embodiments, the techniques described herein relate to a method, further including establishing, through inline cost management, daily budget limits for costs associated with artificial intelligence model usage, and once a limit has been reached, prompting the user and preventing entry of additional prompts unless the user resets the daily budget or provides an override.

[0021] In embodiments, the techniques described herein relate to a method, further including accessing model cost data to determine relative or absolute costs of utilizing different artificial intelligence models, allowing the system to balance quality requirements against budget constraints.

[0022] In embodiments, the techniques described herein relate to a method, further including accessing context window data containing information from previous or intermediate responses from same or different models, enabling the system to maintain coherent multi-turn conversations.

[0023] In embodiments, the techniques described herein relate to a method, further including automatically selecting settings for historical ranking selector, prompt decomposition and chaining, automated response challenging, and model consensus ranking based on complexity level of a prompt as determined from prompt metadata, model cost tiering setting, and model smart router processing sophistication setting.

[0024] In embodiments, the techniques described herein relate to a unified multi-model user interface system for interacting with an artificial intelligence model unification platform including: a user interface configured to serve as a point of interaction between users and the artificial intelligence model unification platform, wherein the user interface presents a text input field where users enter prompts and displays responses generated by artificial intelligence models; a dashboard configured to provide a visual interface presenting information and metrics associated with artificial intelligence model usage, including usage statistics and performance metrics; a web interface configmed to provide a browser-based graphical user interface through which users interact with the platform; browser integration configured to extend functionality of the platform into web browsers through browser extensions; embedded multi-model copilots configured to provide artificial intelligence assistance integrated directly into applications and workflows where users are working; and office productivity software plugins configured to integrate the platform with office productivity applications, wherein the office productivity software plugins include spreadsheet plugins, word processing plugins, and presentation plugins.Atty Dkt: 11016-8049PCT

[0025] In embodiments, the techniques described herein relate to a system, wherein the user interface is configured to enable users to select specific artificial intelligence models from a plurality of available models and displays model selection options organized by capability, cost tier, or performance characteristics.

[0026] In embodiments, the techniques described herein relate to a system, wherein the dashboard is configured to display usage statistics showing number of prompts submitted, number of responses generated, distribution of prompts across different artificial intelligence models, and total costs incurred.

[0027] In embodiments, the techniques described herein relate to a system, wherein the dashboard is configured to present performance metrics including response times, hallucination rates, accuracy scores, and user satisfaction ratings.

[0028] In embodiments, the techniques described herein relate to a system, wherein the dashboard is configured to display sustainability metrics showing carbon footprint of artificial intelligence operations and environmental impact of model usage.

[0029] In embodiments, the techniques described herein relate to a system, wherein the web interface is implemented using hypertext markup language, cascading style sheets, and JavaScript, enabling access from any device with a modem web browser without requiring installation of specialized software.

[0030] In embodiments, the techniques described herein relate to a system, wherein the browser integration is configured to enable users to invoke artificial intelligence models directly from web pages they are viewing, allowing users to summarize web page content, translate text, or answer questions about displayed information.

[0031] In embodiments, the techniques described herein relate to a system, wherein the embedded multi-model copilots are configured to appear as inline assistants within text editors, code editors, or email clients, offering suggestions, completing text, answering questions, and performing tasks based on user's current context.

[0032] In embodiments, the techniques described herein relate to a system, wherein the spreadsheet plugins are configured to integrate with spreadsheet applications to provide artificial intelligence-powered data analysis, formula generation, chart creation, and data visualization capabilities.

[0033] In embodiments, the techniques described herein relate to a system, wherein the spreadsheet plugins are configured to enable users to generate formulas by describing desired calculations in natural language, automatically detect and correct errors in formulas, and generate explanatory comments for complex calculations.

[0034] In embodiments, the techniques described herein relate to a method for providing unified multi-model user interfaces including: presenting, by a user interface, a text input field where users enter prompts and displaying responses generated by artificial intelligence models; providing, by a dashboard, a visual interface presenting usage statistics showing number of prompts submitted, number of responses generated, distribution of prompts across different artificial intelligence models, and total costs incurred; providing, by a web interface, a browser-based graphical user interface implemented using hypertext markup language, cascading style sheets, and JavaScript; extending, by browser integration, functionality of an artificial intelligence platform into web browsers to enable users to invoke artificial intelligence models directly from web pages; providing, by embedded multi-model copilots, artificial intelligence assistance integrated directly into applications as inline assistants; and integrating, by office productivity software plugins, the artificial intelligence platform with office productivity applications including spreadsheet applications, word processing applications, and presentation applications.Atty Dkt: 11016-8049PCT

[0035] In embodiments, the techniques described herein relate to a method, further including enabling users to select specific artificial intelligence models from a plurality of available models and displaying model selection options organized by capability, cost tier, or performance characteristics.

[0036] In embodiments, the techniques described herein relate to a method, further including presenting, by the dashboard, performance metrics including response times, hallucination rates, accuracy scores, user satisfaction ratings, and sustainability metrics showing carbon footprint of artificial intelligence operations.

[0037] In embodiments, the techniques described herein relate to a method, further including enabling, by the browser integration, users to summarize web page content, translate text, answer questions about displayed information, or perform other artificial intelligence-assisted tasks without navigating away from the web page.

[0038] In embodiments, the techniques described herein relate to a method, further including capturing, by the browser integration, selected text from web pages and automatically including that text as context when submitting prompts to artificial intelligence models.

[0039] In embodiments, the techniques described herein relate to a method, further including monitoring, by the embedded multi-model copilots, user actions and automatically invoking appropriate artificial intelligence models to provide contextually relevant assistance without requiring explicit prompts.

[0040] In embodiments, the techniques described herein relate to a method, further including providing, by spreadsheet plugins, artificial intelligence-powered data analysis, formula generation, chart creation, and data visualization capabilities by enabling users to generate formulas by describing desired calculations in natural language.

[0041] In embodiments, the techniques described herein relate to a method, further including providing, by word processing plugins, artificial intelligence-powered writing assistance, document summarization, tone adjustment, and content generation capabilities.

[0042] In embodiments, the techniques described herein relate to a method, further including providing, by presentation plugins, artificial intelligence-powered slide generation, content organization, design suggestions, and speaker notes creation.

[0043] In embodiments, the techniques described herein relate to a method, further including implementing, by a user experience system, consistent interaction patterns, visual design elements, and terminology across the web interface, browser integration, embedded copilots, and office productivity software plugins.

[0044] In embodiments, the techniques described herein relate to a custom integration and frameworks system for integrating an artificial intelligence model unification platform with external applications including: a customizable integration framework configured to provide a flexible architecture enabling users to configure integrations between the platform and external systems without requiring extensive custom software development; a modular use case implementation configured to provide a structured approach to deploying the platform for specific business scenarios by breaking complex use cases into manageable, reusable components; an extract-transform-load system configured to extract data from source systems, transform the data into formats suitable for artificial intelligence model processing, and load the data into storage systems accessible to the platform; software development kits providing libraries, code samples, documentation, and tools enabling software developers to build applications and integrations with the platform; and an application programming interface providing programmatic interfaces through which external applications interact with the platform.Atty Dkt: 11016-8049PCT

[0045] In embodiments, the techniques described herein relate to a system, wherein the customizable integration framework implements a visual workflow designer enabling business users to create integration workflows by connecting pre-built integration components in a graphical interface.

[0046] In embodiments, the techniques described herein relate to a system, wherein the customizable integration framework provides libraries of pre-built connectors for enterprise software systems, cloud services, and data sources, each connector implementing authentication mechanisms, data access methods, and data transformation operations specific to that system.

[0047] In embodiments, the techniques described herein relate to a system, wherein the customizable integration framework enables users to define data mapping rules specifying how fields in source systems correspond to fields in destination systems and how data values should be transformed.

[0048] In embodiments, the techniques described herein relate to a system, wherein the modular use case implementation defines use case templates capturing common patterns of artificial intelligence model usage, required data sources, expected outputs, and success criteria.

[0049] In embodiments, the techniques described herein relate to a system, wherein the modular use case implementation enables users to select use case templates, configure parameters specific to their business context, and deploy functional implementations without starting from scratch.

[0050] In embodiments, the techniques described herein relate to a system, wherein the extract-transform-load system connects to databases, data warehouses, file systems, application programming interfaces, and other data sources to extract data on scheduled intervals or in response to triggering events.

[0051] In embodiments, the techniques described herein relate to a system, wherein the extract-transform-load system performs transformation operations including data cleansing removing duplicates and correcting errors, data normalization converting data to consistent formats and units, data enrichment adding derived fields or supplemental information, data aggregation summarizing detailed data at higher levels of granularity, and data filtering removing irrelevant or sensitive information.

[0052] In embodiments, the techniques described herein relate to a system, wherein the software development kits are available for multiple programming languages including Python, Java, JavaScript, C#, Ruby, and Go, enabling developers to work in their preferred languages.

[0053] In embodiments, the techniques described herein relate to a system, wherein the software development kit for Python includes a client library encapsulating application programming interface calls to the platform, providing Python functions for submitting prompts, retrieving responses, managing settings, and accessing historical data.

[0054] In embodiments, the techniques described herein relate to a method for integrating an artificial intelligence model unification platform with external applications including: providing, by a customizable integration framework, a flexible architecture enabling users to configure integrations between the platform and external systems through a visual workflow designer; providing, by a modular use case implementation, use case templates capturing common patterns of artificial intelligence model usage, required data sources, expected outputs, and success criteria; extracting, by an extract-transform-load system, data from databases, data warehouses, file systems, and application programming interfaces; transforming, by the extract-transform-load system, the extracted data through data cleansing, data normalization, data enrichment, data aggregation, and data filtering; loading, by the extract-transform- load system, transformed data into storage systems accessible to the platform; providing, by software development kits, libraries and code samples in multiple programming languages enabling developers to build applications andAtty Dkt: 11016-8049PCT integrations; and providing, by an application programming interface, programmatic interfaces through which external applications submit prompts, retrieve responses, query historical prompts and responses, and manage configuration settings.

[0055] In embodiments, the techniques described herein relate to a method, further including enabling users to create integration workflows by connecting pre-built integration components in a graphical interface without requiring extensive custom software development.

[0056] In embodiments, the techniques described herein relate to a method, further including providing libraries of pre-built connectors for popular enterprise software systems, cloud services, and data sources, each connector implementing authentication mechanisms, data access methods, and data transformation operations specific to that system.

[0057] In embodiments, the techniques described herein relate to a method, further including enabling users to define data mapping rules specifying how fields in source systems correspond to fields in destination systems and how data values should be transformed including converting date formats, normalizing text, or applying business rules.

[0058] In embodiments, the techniques described herein relate to a method, further including enabling users to select use case templates, configure parameters specific to their business context, and deploy functional implementations rapidly.

[0059] In embodiments, the techniques described herein relate to a method, further including implementing, by the extract-transform-load system, incremental loading strategies identifying and processing only data that has changed since last extraction, reducing processing time and resource consumption.

[0060] In embodiments, the techniques described herein relate to a method, further including maintaining, by the extract-transform-load system, metadata about data lineage tracking which source systems provided data, what transformations were applied, when data was loaded, and which downstream systems consumed the data.

[0061] In embodiments, the techniques described herein relate to a method, further including providing, by the software development kits, command-line tools enabling developers to interact with the platform from terminal environments, submit test prompts, inspect responses, manage application programming interface keys, and monitor usage metrics.

[0062] In embodiments, the techniques described herein relate to a method, further including implementing, by the application programming interface, a RESTful web service using HTTP methods and returning responses in JSON format following standard REST conventions for resource naming and status code usage.

[0063] In embodiments, the techniques described herein relate to a method, further including implementing, by the application programming interface, rate limiting to prevent individual clients from consuming excessive resources.

[0064] In embodiments, the techniques described herein relate to a data and intelligence dynamics system for managing data movement, transformation, storage, and processing operations supporting artificial intelligence model operations including: a staging, switching, and routing system configured to manage movement of data through a platform from sources to processing systems to destinations, wherein the staging system temporarily stores data in intermediate storage locations; a sensor and data fusion system configured to combine data from multiple sensors or data sources to produce more accurate, complete, or reliable information than could be obtained from any single source; edge systems configmed to perform computation and data processing at network edge, close to data sources and end users; databases and feeds configured to provide persistent storage and real-time data streams that the platform accesses during operation; an outcome tracking system configured to monitor results achieved through use of theAtty Dkt: 11016-8049PCT platform by measuring key performance indicators; machine learning, artificial intelligence, and trained neural networks configured to provide predictive and analytical capabilities; and intelligent agents configured as autonomous software entities trained to perform specific tasks by monitoring environments for triggering conditions, making decisions about actions to take, executing actions, and learning from outcomes.

[0065] In embodiments, the techniques described herein relate to a system, wherein the staging system implements storage tiers with different performance and cost characteristics, automatically moving data between fast storage and slower storage based on access patterns and age of data.

[0066] In embodiments, the techniques described herein relate to a system, wherein the switching system routes data from one point to another within the platform, directing data flows based on routing rules, data characteristics, and destination availability, and implements load balancing distributing data processing across multiple servers or processing nodes.

[0067] In embodiments, the techniques described herein relate to a system, wherein the routing system determines optimal paths for data to travel from sources to destinations, considering factors including network bandwidth, latency, cost, and data privacy requirements.

[0068] In embodiments, the techniques described herein relate to a system, wherein the sensor and data fusion system receives data from heterogeneous sensors measuring different physical phenomena and implements Kalman filtering combining noisy measurements from multiple sensors with predictions from physical models to estimate system states with reduced uncertainty.

[0069] In embodiments, the techniques described herein relate to a system, wherein the edge systems include edge servers deployed in cellular base stations, retail stores, factories, or vehicles that process data locally to reduce latency, bandwidth consumption, and dependence on network connectivity to cloud services.

[0070] In embodiments, the techniques described herein relate to a system, wherein the edge systems execute lightweight artificial intelligence models that perform inference operations locally, sending only results or summarized data to cloud systems.

[0071] In embodiments, the techniques described herein relate to a system, wherein the databases include relational databases implementing SQL for structured data storage, NoSQL databases for flexible schema and horizontal scalability, and time-series databases optimized for storing and querying temporal data.

[0072] In embodiments, the techniques described herein relate to a system, wherein the outcome tracking system captures outcome data including task completion rates, accuracy of artificial intelligence-generated responses compared to ground truth, user satisfaction scores, business metrics, and operational metrics.

[0073] In embodiments, the techniques described herein relate to a system, wherein the outcome tracking system implements A / B testing frameworks that randomly assign incoming prompts to different processing strategies, compare outcomes across strategies, and identify statistically significant performance differences.

[0074] In embodiments, the techniques described herein relate to a method for managing data and intelligence dynamics including: managing, by a staging system, movement of data through a platform by temporarily storing data in intermediate storage locations before the data is moved to final destinations; routing, by a switching system, data from one point to another within the platform by directing data flows based on routing rules, data characteristics, and destination availability; combining, by a sensor and data fusion system, data from multiple sensors or data sources to produce more accurate information than could be obtained from any single source; processing, by edge systems, data locally at network edge to reduce latency and bandwidth consumption; providing, by databases and feeds, persistentAtty Dkt: 11016-8049PCT storage and real-time data streams; monitoring, by an outcome tracking system, results achieved through use of the platform by measuring key performance indicators including task completion rates and accuracy of artificial intelligence-generated responses; providing, by machine learning and artificial intelligence systems, predictive and analytical capabilities; and performing, by intelligent agents, specific tasks by monitoring environments for triggering conditions, making decisions about actions, executing actions, and learning from outcomes.

[0075] In embodiments, the techniques described herein relate to a method, further including implementing, by the staging system, storage tiers with different performance and cost characteristics, automatically moving data between fast storage and slower storage based on access patterns and age of data.

[0076] In embodiments, the techniques described herein relate to a method, further including implementing, by the switching system, load balancing that distributes data processing across multiple servers or processing nodes, monitors health and performance of processing nodes, and automatically redirects traffic away from failed or overloaded nodes.

[0077] In embodiments, the techniques described herein relate to a method, further including implementing, by the routing system, policy -based routing applying business rules to determine data paths, such as requiring that personally identifiable information remains within specific geographic regions.

[0078] In embodiments, the techniques described herein relate to a method, further including receiving, by the sensor and data fusion system, data from heterogeneous sensors measuring different physical phenomena, such as combining visual data from cameras with distance measurements from lidar sensors and motion data from accelerometers.

[0079] In embodiments, the techniques described herein relate to a method, further including executing, by the edge systems, lightweight artificial intelligence models that perform inference operations locally, sending only results or summarized data to cloud systems, thereby reducing data transmission costs.

[0080] In embodiments, the techniques described herein relate to a method, further including implementing, by the edge systems, model caching that stores frequently used artificial intelligence models locally and implementing federated learning that trains artificial intelligence models using data distributed across edge devices without transmitting raw data to centralized servers.

[0081] In embodiments, the techniques described herein relate to a method, further including associating, by the outcome tracking system, outcomes with specific artificial intelligence models, prompt processing strategies, and configurations used, enabling analysis of which approaches yield best results.

[0082] In embodiments, the techniques described herein relate to a method, further including analyzing, by intelligent agents, incoming support tickets, classifying tickets by urgency and topic, routing tickets to appropriate specialists, and suggesting responses based on historical resolution patterns.

[0083] In embodiments, the techniques described herein relate to a method, further including implementing, by a software-defined optimization system, programmatic control over system resources and configurations to achieve performance objectives including dynamic routing based on network conditions and application priorities.

[0084] In embodiments, the techniques described herein relate to an agentic processing system for implementing agent-based approaches to prompt processing including: a prompt agentic processing system configmed to utilize and create agents based on frequency and commonality of agent usage patterns, wherein the prompt agentic processing system analyzes incoming prompts to identify sub-tasks that could be handled by specialized agents; a proprietary knowledge agent catalog containing previously created agents organized by function and domain; a user dialog system configured to validate assumptions of agentic approach and solicit further necessary detail, wherein after an artificialAtty Dkt: 11016-8049PCT intelligence model decomposes a prompt and generates agents to handle specific tasks, the user dialog system initiates dialog to confirm that agent decomposition assumptions align with user expectations; and an agent decomposition and refinement system configured to break down agents into finer-grained components and improve agent performance.

[0085] In embodiments, the techniques described herein relate to a system, wherein the prompt agentic processing system searches the proprietary knowledge agent catalog for existing agents matching identified sub-tasks and invokes matching agents to process corresponding portions of prompts.

[0086] In embodiments, the techniques described herein relate to a system, wherein the prompt agentic processing system creates new agents by submitting meta-prompts to artificial intelligence models requesting agent definitions for specific sub-tasks when incoming prompts require capabilities not covered by existing agents.

[0087] In embodiments, the techniques described herein relate to a system, wherein the prompt agentic processing system tracks agent usage frequency, identifies agents invoked repeatedly, and promotes frequently used agents to the proprietary knowledge agent catalog for reuse in future prompt processing.

[0088] In embodiments, the techniques described herein relate to a system, wherein the prompt agentic processing system monitors commonality of agent types across different prompts and use cases, identifies agent capabilities applicable to multiple domains, and generalizes agents to broaden their applicability.

[0089] In embodiments, the techniques described herein relate to a system, wherein the prompt agentic processing system breaks down complex prompts into hierarchies of sub-prompts, creates agent definitions specifying inputs each agent requires, processing each agent performs, and outputs each agent produces, and establishes dependencies between agents.

[0090] In embodiments, the techniques described herein relate to a system, wherein the user dialog system presents agent decomposition plans to users, explaining which sub-tasks have been identified and how each will be addressed, enabling users to verify that decomposition captures their intent.

[0091] in embodiments, the techniques described herein relate to a system, wherein the user dialog system explicitly states assumptions when agent decomposition makes assumptions about ambiguous aspects of prompts and requests user confirmation or correction.

[0092] In embodiments, the techniques described herein relate to a system, wherein the user dialog system prompts users for additional information about proposed agent decomposition, seeking any overlooked aspects, nuances, or details that could improve agent accuracy and precision.

[0093] In embodiments, the techniques described herein relate to a system, wherein the agent decomposition and refinement system analyzes complex agents to identify opportunities for further subdivision, creating sub-agents that handle narrower scopes of functionality and enable more specialized processing.

[0094] In embodiments, the techniques described herein relate to a method for agentic processing including: analyzing, by a prompt agentic processing system, incoming prompts to identify sub-tasks that could be handled by specialized agents; searching, by the prompt agentic processing system, a proprietary knowledge agent catalog for existing agents matching identified sub-tasks; invoking, by the prompt agentic processing system, matching agents to process corresponding portions of prompts; creating, by the prompt agentic processing system, new agents by submitting meta-prompts to artificial intelligence models requesting agent definitions for specific sub-tasks when incoming prompts require capabilities not covered by existing agents; tracking, by the prompt agentic processing system, agent usage frequency and promoting frequently used agents to the proprietary knowledge agent catalog;Atty Dkt: f f0f6-8049PCT initiating, by a user dialog system, dialog to confirm that agent decomposition assumptions align with user expectations; and refining, by an agent decomposition and refinement system, existing agent definitions based on performance observations.

[0095] In embodiments, the techniques described herein relate to a method, further including monitoring, by the prompt agentic processing system, commonality of agent types across different prompts and use cases, identifying agent capabilities applicable to multiple domains, and generalizing agents to broaden applicability.

[0096] In embodiments, the techniques described herein relate to a method, further including breaking down, by the prompt agentic processing system, complex prompts into hierarchies of sub-prompts, creating agent definitions specifying inputs each agent requires, processing each agent performs, and outputs each agent produces.

[0097] in embodiments, the techniques described herein relate to a method, further including establishing, by the prompt agentic processing system, dependencies between agents indicating which agent outputs serve as inputs to other agents and coordinating agent execution by invoking independent agents in parallel.

[0098] In embodiments, the techniques described herein relate to a method, further including presenting, by the user dialog system, agent decomposition plans to users, explaining which sub-tasks have been identified and how each will be addressed, enabling users to verify that decomposition captures their intent.

[0099] in embodiments, the techniques described herein relate to a method, further including explicitly stating, by the user dialog system, assumptions when agent decomposition makes assumptions about ambiguous aspects of prompts and requesting user confirmation or correction.

[0100] In embodiments, the techniques described herein relate to a method, further including prompting, by the user dialog system, users for additional information about proposed agent decomposition, seeking any overlooked aspects, nuances, or details that could improve agent accuracy and precision.

[0101] In embodiments, the techniques described herein relate to a method, further including analyzing, by the agent decomposition and refinement system, complex agents to identify opportunities for further subdivision and creating sub-agents that handle narrower scopes of functionality.

[0102] In embodiments, the techniques described herein relate to a method, further including modifying, by the agent decomposition and refinement system, agent logic to handle edge cases that caused failures, adjusting agent parameters to improve output quality, and updating agent model selections when newer models demonstrate better performance.

[0103] In embodiments, the techniques described herein relate to a method, further including implementing, by the agent decomposition and refinement system, feedback loops where agent outputs are evaluated against ground truth or user corrections, agents that consistently produce errors are flagged for refinement, and refinement operations modify agent definitions to address identified deficiencies.

[0104] In embodiments, the techniques described herein relate to a models as agents system for treating artificial intelligence models as autonomous agents including: an agent interface wrapper configured to wrap artificial intelligence models with agent interfaces standardizing how models are invoked, how inputs are provided, how outputs are retrieved, and how errors are handled; a role definition system configured to configure models with role definitions specifying types of tasks each model handles, input formats each model accepts, output formats each model generates, and quality characteristics users can expect from each model; a model agent communication protocol configured to enable models to request assistance from other models when encountering inputs beyond their capabilities, delegate sub-tasks to more specialized models, and negotiate with other models to resolve conflicts whenAtty Dkt: 11016-8049PCT different models generate inconsistent outputs; and a composite agent system configured to configure groups of models as composite agents that present unified interfaces while internally distributing work across multiple models.

[0105] In embodiments, the techniques described herein relate to a system, wherein the agent interface wrapper enables models to be composed into processing pipelines where outputs from one model serve as inputs to another.

[0106] In embodiments, the techniques described herein relate to a system, wherein the role definition system indicates that a particular model specializes in text summarization, accepting long-form documents as input and generating concise summaries as output, while another model specializes in sentiment analysis, accepting text samples as input and generating sentiment classifications with confidence scores as output.

[0107] In embodiments, the techniques described herein relate to a system, wherein the role definition system assigns models to agent teams where multiple models collaborate to accomplish complex tasks, with each model contributing its specialized capabilities.

[0108] In embodiments, the techniques described herein relate to a system, wherein a model agent encountering a prompt requesting both text generation and image creation recognizes that it lacks image generation capabilities, invokes an image generation model agent to handle the image portion, and combines outputs from both agents to produce a complete response.

[0109] In embodiments, the techniques described herein relate to a system, wherein the model agent communication protocol tracks model agent performance, measuring task completion rates, output quality scores, and resource consumption, using performance data to inform model selection decisions.

[0110] In embodiments, the techniques described herein relate to a system, wherein the composite agent system includes a composite agent for multilingual translation including multiple translation models specialized for different language pairs, automatically routing translation requests to appropriate specialized models based on source and target languages.

[0111] In embodiments, the techniques described herein relate to a system, wherein the composite agent system includes a composite agent for financial analysis including models for numerical calculation, market trend prediction, risk assessment, and narrative generation, orchestrating these models to produce comprehensive financial analyses.

[0112] In embodiments, the techniques described herein relate to a system, wherein the composite agent system configures composite agents with fallback logic that attempts alternative models when primary models fail or produce low-quality outputs.

[0113] In embodiments, the techniques described herein relate to a system, wherein the agent interface wrapper standardizes error handling across models having different native error reporting mechanisms.

[0114] In embodiments, the techniques described herein relate to a method for treating artificial intelligence models as autonomous agents including: wrapping, by an agent interface wrapper, artificial intelligence models with agent interfaces standardizing how models are invoked, how inputs are provided, how outputs are retrieved, and how errors are handled; configuring, by a role definition system, models with role definitions specifying types of tasks each model handles, input formats each model accepts, output formats each model generates, and quality characteristics users can expect; enabling, by a model agent communication protocol, models to request assistance from other models when encountering inputs beyond their capabilities and to delegate sub-tasks to more specialized models; configuring, by a composite agent system, groups of models as composite agents that present unified interfaces while internally distributing work across multiple models; and tracking, by a performance monitoring system, model agent performance including task completion rates, output quality scores, and resource consumption.Atty Dkt: 11016-8049PCT

[0115] In embodiments, the techniques described herein relate to a method, further including enabling, by the agent interface wrapper, models to be composed into processing pipelines where outputs from one model serve as inputs to another.

[0116] In embodiments, the techniques described herein relate to a method, further including indicating, by the role definition system, that a particular model specializes in a specific task type and assigning models to agent teams where multiple models collaborate to accomplish complex tasks.

[0117] In embodiments, the techniques described herein relate to a method, further including invoking, by a model agent encountering a prompt requesting multiple types of content, specialized model agents to handle portions requiring capabilities the model agent lacks and combining outputs from multiple agents to produce complete responses.

[0118] In embodiments, the techniques described herein relate to a method, further including negotiating, by model agents through the communication protocol, with other models to resolve conflicts when different models generate inconsistent outputs.

[0119] In embodiments, the techniques described herein relate to a method, further including, by the composite agent system, a composite agent for multilingual translation with multiple translation models specialized for different language pairs and automatically routing translation requests to appropriate specialized models.

[0120] In embodiments, the techniques described herein relate to a method, further including, by the composite agent system, a composite agent for financial analysis with models for numerical calculation, market trend prediction, risk assessment, and narrative generation, orchestrating these models to produce comprehensive analyses.

[0121] In embodiments, the techniques described herein relate to a method, further including configuring, by the composite agent system, composite agents with fallback logic that attempts alternative models when primary models fail or produce low-quality outputs, improving reliability and robustness.

[0122] In embodiments, the techniques described herein relate to a method, further including using, by a model selection system, performance data including task completion rates, output quality scores, and resource consumption to inform model selection decisions.

[0123] In embodiments, the techniques described herein relate to a method, further including decoupling, by the agent interface wrapper, prompt processing logic from specific model implementations, enabling models to be replaced or upgraded without modifying prompt processing workflows.

[0124] In embodiments, the techniques described herein relate to a model-user prompt interaction system for enabling artificial intelligence models to ask clarifying questions including: a prompt interception system configured to intercept prompts before models process them and analyze prompts for ambiguities, uncertainties, or missing information; a clarifying question generator configured to generate clarifying questions that help resolve identified ambiguities, uncertainties, or missing information; a user dialog interface configured to present questions to users and receive user responses; a prompt augmentation system configured to augment original prompts with clarifying information received from users before submitting prompts to models for processing; and a validation system configmed to validate model responses by generating challenge questions assessing whether model outputs properly address user intent as clarified through dialog.

[0125] In embodiments, the techniques described herein relate to a system, wherein the prompt interception system identifies ambiguous terminology in prompts where words or phrases could be interpreted in multiple ways.Atty Dkt: 11016-8049PCT

[0126] In embodiments, the techniques described herein relate to a system, wherein the clarifying question generator generates questions requesting users to specify intended meanings when ambiguous terminology is identified and incorporates user selections into prompt context.

[0127] In embodiments, the techniques described herein relate to a system, wherein the prompt interception system identifies missing information required to fully address prompts, such as prompts requesting analysis of data without specifying which data to analyze or prompts requesting generation of content without specifying desired tone or style.

[0128] In embodiments, the techniques described herein relate to a system, wherein the clarifying question generator generates questions requesting missing information and presents questions to users in priority order with most important questions presented first.

[0129] In embodiments, the techniques described herein relate to a system, wherein the prompt interception system detects implicit assumptions in prompts that may not align with user intent and explicitly states detected assumptions.

[0130] In embodiments, the techniques described herein relate to a system, wherein the user dialog interface enables users to confirm assumptions or provide corrections and the prompt augmentation system adjusts prompt context based on user feedback.

[0131] In embodiments, the techniques described herein relate to a system, wherein the prompt interception system identifies branching points where prompts could proceed down multiple paths depending on user preferences, and the user dialog interface presents alternative paths to users for selection.

[0132] In embodiments, the techniques described herein relate to a system, wherein the system implements progressive clarification where initial questions address high-level aspects of prompts and subsequent questions drill down into details.

[0133] In embodiments, the techniques described herein relate to a system, wherein when processing a prompt requesting creation of a presentation, the system first asks about the presentation's purpose and audience, then asks about desired length and structure, then asks about visual style preferences and specific content to include.

[0134] In embodiments, the techniques described herein relate to a method for model-user prompt interaction including: intercepting, by a prompt interception system, prompts before models process them; analyzing, by the prompt interception system, prompts for ambiguities, uncertainties, or missing information; generating, by a clarifying question generator, clarifying questions that help resolve identified ambiguities, uncertainties, or missing information; presenting, by a user dialog interface, questions to users; receiving, by the user dialog interface, user responses; augmenting, by a prompt augmentation system, original prompts with clarifying information received from users; and validating, by a validation system, model responses by generating challenge questions assessing whether model outputs properly address user intent as clarified through dialog.

[0135] In embodiments, the techniques described herein relate to a method, further including identifying, by the prompt interception system, ambiguous terminology in prompts where words or phrases could be interpreted in multiple ways and generating questions requesting users to specify intended meanings.

[0136] In embodiments, the techniques described herein relate to a method, further including identifying, by the prompt interception system, missing information required to fully address prompts and generating questions requesting missing information presented to users in priority order.

[0137] In embodiments, the techniques described herein relate to a method, further including detecting, by the prompt interception system, implicit assumptions in prompts that may not align with user intent and explicitly stating detected assumptions.Atty Dkt: 11016-8049PCT

[0138] In embodiments, the techniques described herein relate to a method, further including enabling, by the user dialog interface, users to confirm assumptions or provide corrections and adjusting prompt context based on user feedback.

[0139] In embodiments, the techniques described herein relate to a method, further including identifying, by the prompt interception system, branching points where prompts could proceed down multiple paths depending on user preferences and presenting alternative paths to users for selection.

[0140] In embodiments, the techniques described herein relate to a method, further including implementing progressive clarification where initial questions address high-level aspects of prompts and subsequent questions drill down into details based on earlier responses.

[0141] In embodiments, the techniques described herein relate to a method, further including learning from historical interactions, identifying questions that users frequently skip or questions that consistently receive same responses, and adjusting question generation to prioritize valuable questions.

[0142] In embodiments, the techniques described herein relate to a method, further including reviewing, by the validation system, clarifications provided by the user, identifying aspects of user intent that the response should address, generating questions assessing whether the response adequately addresses those aspects, and prompting for model revision if deficiencies are detected.

[0143] In embodiments, the techniques described herein relate to a method, further including avoiding overwhelming users with too many questions simultaneously by implementing progressive clarification and enabling users to skip questions if defaults are acceptable. MODEL SMART ROUTER

[0144] In embodiments, the techniques described herein relate to a model smart router system for intelligently directing prompts to optimal sets of models including: a cache for repetitive requests configured to store previously computed responses to prompts, enabling rapid response when identical or substantially similar prompts are submitted again; a local store configured to optimize utilization of limited context windows in artificial intelligence models by maintaining supplemental storage of information relevant to ongoing conversations or tasks; adaptive learning capabilities configured to enable the model smart router to improve routing decisions and response quality over time through analysis of historical performance data; a unified model access system configured to enable issuance of single prompts to multiple artificial intelligence models simultaneously, with responses efficiently aggregated and delivered from the most suitable model; a response model consensus ranking system configured to implement a process where multiple models generate responses to user-provided prompts and those responses are evaluated by same or different models which rank each response; a model cost tiering system configmed to classify artificial intelligence models into cost tiers based on per-query pricing or computational resource requirements; an automated response challenging system configmed to ask models challenge questions immediately after models provide responses to validate answers and reduce hallucination; and a response processing sophistication system configured to control number and quality of models used during response generation.

[0145] In embodiments, the techniques described herein relate to a system, wherein the cache computes hash values or embeddings for incoming prompts, compares computed values against values for cached prompts to identify matches, and returns cached responses when matches are found, bypassing model invocation.

[0146] In embodiments, the techniques described herein relate to a system, wherein the cache implements cache invalidation policies that remove cached responses after specified time periods, when underlying data used to generate responses changes, or when newer model versions become available.Atty Dkt: 11016-8049PCT

[0147] In embodiments, the techniques described herein relate to a system, wherein the cache implements partial matching that identifies prompts similar but not identical to cached prompts, retrieves cached responses as starting points, and invokes models to refine cached responses to address differences.

[0148] In embodiments, the techniques described herein relate to a system, wherein when conversations span multiple turns and accumulated context exceeds model context window limits, the local store maintains full conversation history and selectively loads relevant portions into model context windows based on current prompt focus.

[0149] In embodiments, the techniques described herein relate to a system, wherein the local store implements intelligent context selection using relevance scoring that ranks historical messages or information segments by relevance to current prompts.

[0150] In embodiments, the techniques described herein relate to a system, wherein the adaptive learning capabilities track which models were selected for prompts, how models performed, what response quality scores were achieved, what costs were incurred, and what user satisfaction ratings resulted.

[0151] In embodiments, the techniques described herein relate to a system, wherein the adaptive learning capabilities analyze patterns in tracking data, identify correlations between prompt characteristics and model performance, and update prompt historical ranked models data with learned associations.

[0152] In embodiments, the techniques described herein relate to a system, wherein the unified model access system broadcasts prompts to sets of models selected by the model smart router, collects responses from all models in parallel, evaluates responses using consensus ranking, selects best responses, and returns selected responses to users.

[0153] In embodiments, the techniques described herein relate to a system, wherein the response model consensus ranking system invokes a set of evaluator models with prompts that include the original user prompt and the set of responses generated by response models, requests evaluator models to score each response on dimensions including accuracy, relevance, clarity, completeness, and conciseness, and aggregates scores across evaluator models.

[0154] In embodiments, the techniques described herein relate to a method for intelligently routing prompts to artificial intelligence models including: storing, by a cache, previously computed responses to prompts and returning cached responses when identical or substantially similar prompts are submitted again; maintaining, by a local store, full conversation history when conversations span multiple turns and selectively loading relevant portions into model context windows based on current prompt focus; improving, by adaptive learning capabilities, routing decisions over time by tracking which models were selected for prompts, how models performed, and analyzing patterns in tracking data; enabling, by a unified model access system, issuance of single prompts to multiple artificial intelligence models simultaneously; implementing, by a response model consensus ranking system, a process where multiple models generate responses and those responses are evaluated by evaluator models which rank each response; classifying, by a model cost tiering system, artificial intelligence models into cost tiers based on per-query pricing; asking, by an automated response challenging system, models challenge questions immediately after models provide responses to validate answers; and controlling, by a response processing sophistication system, number and quality of models used during response generation.

[0155] In embodiments, the techniques described herein relate to a method, further including computing, by the cache, hash values or embeddings for incoming prompts, comparing computed values against values for cached prompts to identify matches, and returning cached responses when matches are found.Atty Dkt: 11016-8049PCT

[0156] In embodiments, the techniques described herein relate to a method, further including implementing, by the cache, cache invalidation policies that remove cached responses after specified time periods or when underlying data used to generate responses changes.

[0157] In embodiments, the techniques described herein relate to a method, further including implementing, by the cache, partial matching that identifies prompts similar but not identical to cached prompts, retrieves cached responses as starting points, and invokes models to refine cached responses.

[0158] In embodiments, the techniques described herein relate to a method, further including implementing, by the local store, intelligent context selection using relevance scoring that ranks historical messages or information segments by relevance to current prompts, ensuring most pertinent information occupies limited context window space.

[0159] In embodiments, the techniques described herein relate to a method, further including implementing, by the local store, context summarization where long conversations are periodically summarized by artificial intelligence models, summaries replace detailed histories in context windows, and detailed histories remain available for retrieval if needed.

[0160] In embodiments, the techniques described herein relate to a method, further including identifying, by the adaptive learning capabilities, correlations between prompt characteristics and model performance, updating prompt historical ranked models data with learned associations, and adjusting model selection logic to favor models that have demonstrated strong performance.

[0161] In embodiments, the techniques described herein relate to a method, further including broadcasting, by the unified model access system, prompts to sets of models, collecting responses from all models in parallel, evaluating responses using consensus ranking, and selecting best responses.

[0162] In embodiments, the techniques described herein relate to a method, further including defining, by the model cost tiering system, tier levels including Level 1 for cheap models, Level 2 for economical models, Level 3 for moderate models, and Level 4 for premium models, and enabling users to specify preferred cost tiers through configuration settings.

[0163] In embodiments, the techniques described herein relate to a method, further including generating, by the automated response challenging system, challenge questions based on prompt challenge data created during prompt pre-processing, submitting challenge questions to models, and comparing challenge responses against original responses to identify inconsistencies. PROMPT PROCESSING

[0164] In embodiments, the techniques described herein relate to an artificial intelligence system including: a prompt processing component configured to: receive a user input including natural language data; extract metadata from the user input, the metadata including at least one of: semantic elements, temporal attributes, and quantitative metrics characterizing processing requirements; decompose the user input into a plurality of interconnected sub-components based on the metadata; and determine a processing sequence for the plurality of sub-components, wherein the processing sequence includes at least one of sequential processing and concurrent processing; a model access component configmed to transmit at least one sub-component to at least one artificial intelligence model for processing; and a response aggregation component configured to combine outputs from processing the plurality of sub-components.

[0165] In embodiments, the techniques described herein relate to a system, wherein the metadata includes semantic elements identifying key terms within the user input.Atty Dkt: 11016-8049PCT

[0166] In embodiments, the techniques described herein relate to a system, wherein the metadata includes temporal attributes indicating at least one of past, present, and future temporal reference.

[0167] In embodiments, the techniques described herein relate to a system, wherein the quantitative metrics include a complexity level assigned on a numerical scale.

[0168] In embodiments, the techniques described herein relate to a system, wherein the prompt processing component is further configmed to separate content data from query data within the user input.

[0169] In embodiments, the techniques described herein relate to a system, wherein the prompt processing component is further configured to identify a content type of the user input, wherein the content type includes at least one of textual data, visual data, graphical data, and document data.

[0170] In embodiments, the techniques described herein relate to a system, further including a sophistication control configured to adjust a number of artificial intelligence models used in processing based on a processing parameter.

[0171] In embodiments, the techniques described herein relate to a system, wherein the sophistication control provides at least three processing levels corresponding to different quantities of artificial intelligence models.

[0172] In embodiments, the techniques described herein relate to a system, wherein the prompt processing component is further configured to generate verification questions associated with the user input and obtain user responses to the verification questions prior to transmitting to the at least one artificial intelligence model.

[0173] In embodiments, the techniques described herein relate to a system, wherein the response aggregation component is configured to: receive a plurality of outputs from a plurality of artificial intelligence models; generate verification data configured to test accuracy of the plurality of outputs; and select an output from the plurality of outputs based on evaluation using the verification data.

[0174] In embodiments, the techniques described herein relate to a method for processing user inputs in an artificial intelligence system, the method including: receiving, by a processing component, a user input including natural language data; extracting, by the processing component, metadata from the user input, the metadata including at least one of: semantic elements, temporal attributes, and quantitative metrics characterizing processing requirements; decomposing, by the processing component, the user input into a plurality of interconnected sub-components based on the metadata; determining, by the processing component, a processing sequence for the plurality of subcomponents, wherein the processing sequence includes at least one of sequential processing and concurrent processing; transmitting, by a model access component, at least one sub-component to at least one artificial intelligence model for processing; and combining, by a response aggregation component, outputs from processing the plurality of sub-components.

[0175] In embodiments, the techniques described herein relate to a method, further including assigning a complexity level to the user input based on at least one of: linguistic characteristics, computational intensity, and resource requirements.

[0176] In embodiments, the techniques described herein relate to a method, further including parsing the user input to identify and separate content data from query data.

[0177] In embodiments, the techniques described herein relate to a method, wherein decomposing the user input includes generating a plurality of sub-queries, wherein each sub-query is associated with at least one other sub-query through a dependency relationship.

[0178] In embodiments, the techniques described herein relate to a method, further including processing sub-queries having no dependency relationships concurrently.Atty Dkt: 11016-8049PCT

[0179] In embodiments, the techniques described herein relate to a method, further including: generating a sample prompt configured to extract the metadata from the user input; transmitting the sample prompt and the user input to a metadata extraction model; and receiving the metadata from the metadata extraction model.

[0180] In embodiments, the techniques described herein relate to a method, further including adjusting a number of artificial intelligence models used in processing based on at least one of: a user setting, the quantitative metrics, and a resource constraint.

[0181] In embodiments, the techniques described herein relate to a method, further including: generating verification questions configured to validate assumptions regarding the user input; presenting the verification questions to a user; receiving user responses to the verification questions; and modifying the plurality of sub-components based on the user responses.

[0182] In embodiments, the techniques described herein relate to a method, further including: receiving a plurality of outputs from a plurality of artificial intelligence models processing the at least one sub-component; generating challenge data configured to evaluate the plurality of outputs; and ranking the plurality of outputs based on evaluation criteria applied using the challenge data.

[0183] In embodiments, the techniques described herein relate to a method, wherein the evaluation criteria include at least one of: accuracy, clarity, conciseness, and completeness.

[0184] In embodiments, the techniques described herein relate to an artificial intelligence platform system including: a prompt pre-processor including: a settings optimizer configured to automatically select configuration parameters based on at least one of: characteristics of an input, resource constraints, and user preferences; a repetition identifier configmed to compare the input with historical inputs to identify processing optimization opportunities; a knowledgegraph retrieval component configured to perform dynamic knowledge integration combining private data sources and public data sources; and an autonomous agent creator configured to generate specialized processing entities for handling specific portions of the input; a system management infrastructure including: a collaboration system enabling multi-user interaction; a financial management system automating resource accounting; and an analytics system tracking performance indicators; a security framework including protective measures configured to safeguard data and control access; and a model integration component configured to provide connectivity to a plurality of external artificial intelligence models.

[0185] In embodiments, the techniques described herein relate to a system, wherein the knowledge-graph retrieval component is configmed to: receive data in a standardized format; generate a knowledge representation structure from the data; and perform hybrid processing including semantic processing and lexical processing.

[0186] In embodiments, the techniques described herein relate to a system, wherein the knowledge-graph retrieval component is configured to dynamically generate the knowledge representation structure without requiring predefined schema definitions.

[0187] In embodiments, the techniques described herein relate to a system, wherein the prompt pre-processor further includes a decomposition engine configured to break down the input into smaller interconnected processing tasks.

[0188] In embodiments, the techniques described herein relate to a system, wherein the repetition identifier is configmed to: calculate a similarity measure between the input and the historical inputs; identify at least one historical input exceeding a similarity threshold; and retrieve a previously generated response associated with the at least one historical input.Atty Dkt: 11016-8049PCT

[0189] In embodiments, the techniques described herein relate to a system, wherein the autonomous agent creator is configured to: track frequency of agent usage patterns; identify recurring agent configmations; and automatically create and catalog new specialized processing entities based on the recurring agent configurations.

[0190] In embodiments, the techniques described herein relate to a system, wherein the system management infrastructure further includes: a reporting system configured to generate documentation on at least one of: usage, configuration, and financial data; a feedback system configured to collect user evaluation data; and a configuration system enabling adjustment of operational parameters.

[0191] In embodiments, the techniques described herein relate to a system, further including a data management infrastructure configured to: manage data sources and data flows; store data in at least one database; and provide data through data feeds.

[0192] In embodiments, the techniques described herein relate to a system, wherein the model integration component includes at least one of: an application programming interface, a software development kit, and an integration framework.

[0193] In embodiments, the techniques described herein relate to a system, further including a microservices architecture enabling independent scaling of functional components.

[0194] In embodiments, the techniques described herein relate to a method for managing an artificial intelligence platform, the method including: receiving, by a prompt pre-processor, an input; automatically selecting, by a settings optimizer, configuration parameters based on at least one of: characteristics of the input, resource constraints, and user preferences; comparing, by a repetition identifier, the input with historical inputs to identify processing optimization opportunities; performing, by a knowledge -graph retrieval component, dynamic knowledge integration combining private data sources and public data sources; generating, by an autonomous agent creator, specialized processing entities for handling specific portions of the input; enabling, by a collaboration system, multi-user interaction; tracking, by an analytics system, performance indicators; and providing, by a model integration component, connectivity to a plurality of external artificial intelligence models.

[0195] In embodiments, the techniques described herein relate to a method, wherein performing dynamic knowledge integration includes: receiving data in a standardized format; generating a knowledge representation structure from the data without requiring pre-defined schema definitions; performing semantic processing on the data; and performing lexical processing on the data.

[0196] In embodiments, the techniques described herein relate to a method, further including: calculating a similarity measure between the input and the historical inputs; determining that at least one historical input exceeds a similarity threshold; and providing a response by retrieving a previously generated response associated with the at least one historical input.

[0197] In embodiments, the techniques described herein relate to a method, further including: tracking frequency of agent usage patterns; identifying recurring agent configmations based on the frequency; and automatically creating and cataloging new specialized processing entities based on the recurring agent configurations.

[0198] In embodiments, the techniques described herein relate to a method, further including: decomposing the input into a plurality of interconnected processing tasks; and assigning at least one specialized processing entity to handle at least one of the plurality of interconnected processing tasks.Atty Dkt: 11016-8049PCT

[0199] In embodiments, the techniques described herein relate to a method, further including: generating documentation on at least one of: usage patterns, configuration data, and financial data; and providing the documentation through a reporting interface.

[0200] In embodiments, the techniques described herein relate to a method, further including: collecting user evaluation data through a feedback system; and utilizing the user evaluation data to optimize at least one of: model selection, processing parameters, and response quality.

[0201] In embodiments, the techniques described herein relate to a method, further including managing data sources and data flows using at least one of: an extract-transform-load process, a data pipeline, and a staging system.

[0202] In embodiments, the techniques described herein relate to a method, further including providing connectivity to the plurality of external artificial intelligence models through at least one of: an application programming interface, a web interface, and a software development kit.

[0203] In embodiments, the techniques described herein relate to a method, further including independently scaling functional components using a microservices architecture.

[0204] In embodiments, the techniques described herein relate to an artificial intelligence system with security infrastructure including: an encryption component configured to apply cryptographic transformation to user inputs and system outputs, wherein cryptographic keys remain exclusively with user clients; an authentication component configured to require multiple verification factors for access; an access control component configmed to manage and restrict access based on roles assigned to users; an audit component configured to capture and store information related to user activities and system events; and a compliance component configured to ensure adherence to data privacy regulations.

[0205] In embodiments, the techniques described herein relate to a system, wherein the encryption component is configured to encode information such that only authorized users with correct decryption keys can access the information.

[0206] In embodiments, the techniques described herein relate to a system, wherein the authentication component is configured to require at least two verification factors selected from: knowledge factors, possession factors, and inherence factors.

[0207] In embodiments, the techniques described herein relate to a system, wherein the access control component is configured to: assign roles to users within an organization; define permissions associated with each role; and enforce the permissions based on the roles.

[0208] In embodiments, the techniques described herein relate to a system, wherein the audit component is configmed to generate audit trails including: timestamps of user activities; identifiers of users performing activities; descriptions of activities performed; and system events associated with the activities.

[0209] In embodiments, the techniques described herein relate to a system, wherein the compliance component is configured to ensure adherence to at least one of: General Data Protection Regulation (GDPR), California Consumer Privacy Act (CCPA), and Health Insurance Portability and Accountability Act (HIPAA).

[0210] In embodiments, the techniques described herein relate to a system, further including an intellectual property protection component configured to safeguard proprietary information processed by the system.

[0211] In embodiments, the techniques described herein relate to a system, wherein the encryption component is configured to maintain encryption during data transmission and during data storage.Atty Dkt: 11016-8049PCT

[0212] In embodiments, the techniques described herein relate to a system, further including a threat detection component configured to identify security threats based on analysis of the information captured by the audit component.

[0213] In embodiments, the techniques described herein relate to a system, wherein the access control component is further configured to implement attribute-based access control based on user attributes and environmental conditions.

[0214] In embodiments, the techniques described herein relate to a method for securing an artificial intelligence system, the method including: applying, by an encryption component, cryptographic transformation to user inputs and system outputs, wherein cryptographic keys remain exclusively with user clients; requiring, by an authentication component, multiple verification factors for access; managing, by an access control component, access restrictions based on roles assigned to users; capturing, by an audit component, information related to user activities and system events; storing, by the audit component, the information in audit logs; and ensuring, by a compliance component, adherence to data privacy regulations.

[0215] In embodiments, the techniques described herein relate to a method, wherein applying cryptographic transformation includes encoding information such that only authorized users with correct decryption keys can access the information.

[0216] In embodiments, the techniques described herein relate to a method, wherein requiring multiple verification factors includes: requesting a first verification factor including at least one of: a password, a PIN, and a security question answer; requesting a second verification factor including at least one of: a hardware token, a mobile device confirmation, and a biometric scan; and granting access only upon successful verification of both factors.

[0217] In embodiments, the techniques described herein relate to a method, wherein managing access restrictions includes: assigning roles to users within an organization; defining permissions associated with each role; and enforcing the permissions by preventing unauthorized actions.

[0218] In embodiments, the techniques described herein relate to a method, wherein capturing information includes recording: timestamps of user activities; identifiers of users performing activities; descriptions of activities performed; and system events associated with the activities.

[0219] In embodiments, the techniques described herein relate to a method, wherein ensuring adherence to data privacy regulations includes: implementing data handling procedures in compliance with at least one regulation; monitoring system operations for compliance violations; and generating compliance reports.

[0220] In embodiments, the techniques described herein relate to a method, further including safeguarding proprietary information by restricting access to proprietary data based on user permissions.

[0221] In embodiments, the techniques described herein relate to a method, further including maintaining cryptographic transformation during data transmission and during data storage.

[0222] In embodiments, the techniques described herein relate to a method, further including: analyzing the information captured in the audit logs; identifying patterns indicative of security threats; and generating alerts in response to identified security threats.

[0223] In embodiments, the techniques described herein relate to a method, further including implementing attributebased access control by evaluating user attributes and environmental conditions prior to granting access.

[0224] In embodiments, the techniques described herein relate to an artificial intelligence analytics system including: a real-time analytics component configured to provide users with concurrent analytics on queries; a query analysis component configured to generate analytic measures related to sets of queries; a performance metrics componentAtty Dkt: 11016-8049PCT configured to determine performance indicators associated with artificial intelligence model operations; a hallucination tracking component configured to measure accuracy metrics of model outputs; a response time monitoring component configured to track latency of model operations; a usage pattern analysis component configured to determine behavioral trends of system utilization; a feedback loop component configured to improve model performance based on at least one of: user interaction data and system metrics; and a reporting component configured to generate reports on usage and billing.

[0225] In embodiments, the techniques described herein relate to a system, wherein the real-time analytics component is configured to provide insights for optimizing model interactions and resource allocation.

[0226] In embodiments, the techniques described herein relate to a system, wherein the performance metrics component is configured to track which artificial intelligence models perform best for specific types of inputs.

[0227] In embodiments, the techniques described herein relate to a system, wherein the hallucination tracking component is configured to: identify factual inaccuracies in model outputs; quantify a rate of factual inaccuracies; and associate hallucination metrics with specific artificial intelligence models.

[0228] In embodiments, the techniques described herein relate to a system, wherein the response time monitoring component is configured to: measure processing duration for individual queries; calculate average latency for different artificial intelligence models; and utilize latency data as a factor in model selection.

[0229] In embodiments, the techniques described herein relate to a system, wherein the usage pattern analysis component is configured to identify: temporal patterns of system usage; user-specific usage characteristics; and model-specific utilization trends.

[0230] In embodiments, the techniques described herein relate to a system, wherein the feedback loop component configured to improve model performance includes: a user feedback mechanism configured to collect explicit user evaluations; and a metrics based feedback mechanism configured to automatically adjust system parameters based on cost metrics, response time metrics, query metrics, performance metrics, hallucination metrics, and sustainability metrics.

[0231] In embodiments, the techniques described herein relate to a system, wherein the reporting component is configured to generate detailed reports on at least one of: daily usage, weekly usage, and monthly usage.

[0232] In embodiments, the techniques described herein relate to a system, further including a usage tracking component configured to monitor system utilization for billing purposes.

[0233] In embodiments, the techniques described herein relate to a system, further including a model improvement component configured to refine selection algorithms over time based on the feedback loop component.

[0234] In embodiments, the techniques described herein relate to a method for analytics in an artificial intelligence system, the method including: providing, by a real-time analytics component, concurrent analytics on queries to users; generating, by a query analysis component, analytic measures related to sets of queries; determining, by a performance metrics component, performance indicators associated with artificial intelligence model operations; measuring, by a hallucination tracking component, accuracy metrics of model outputs; tracking, by a response time monitoring component, latency of model operations; determining, by a usage pattern analysis component, behavioral trends of system utilization; improving, by a feedback loop component, model performance based on at least one of: user interaction data and system metrics; and generating, by a reporting component, reports on usage and billing.

[0235] In embodiments, the techniques described herein relate to a method, wherein providing concurrent analytics includes providing insights for optimizing model interactions and resource allocation.Atty Dkt: 11016-8049PCT

[0236] In embodiments, the techniques described herein relate to a method, wherein determining performance indicators includes tracking which artificial intelligence models perform best for specific types of inputs.

[0237] In embodiments, the techniques described herein relate to a method, wherein measuring accuracy metrics includes: identifying factual inaccuracies in model outputs; quantifying a rate of factual inaccuracies; and associating hallucination metrics with specific artificial intelligence models.

[0238] In embodiments, the techniques described herein relate to a method, wherein tracking latency includes: measuring processing duration for individual queries; calculating average latency for different artificial intelligence models; and utilizing latency data as a factor in model selection.

[0239] In embodiments, the techniques described herein relate to a method, wherein determining behavioral trends includes identifying: temporal patterns of system usage; user-specific usage characteristics; and model-specific utilization trends.

[0240] In embodiments, the techniques described herein relate to a method, wherein improving model performance includes: collecting explicit user evaluations through a user feedback mechanism; and automatically adjusting system parameters based on cost metrics, response time metrics, query metrics, performance metrics, hallucination metrics, and sustainability metrics.

[0241] In embodiments, the techniques described herein relate to a method, wherein generating reports includes producing detailed reports on at least one of: daily usage, weekly usage, and monthly usage to promote transparency and accountability.

[0242] In embodiments, the techniques described herein relate to a method, further including: monitoring system utilization for billing purposes; calculating costs based on resource consumption; and automating charging of users based on the costs.

[0243] In embodiments, the techniques described herein relate to a method, further including refining selection algorithms over time based on accumulated feedback data.

[0244] In embodiments, the techniques described herein relate to an artificial intelligence system with environmental monitoring including: a carbon footprint analytics component configmed to determine environmental impact analytics for artificial intelligence operations, wherein the environmental impact analytics include monitoring and analyzing CO2 emissions; a computational resource tracking component configured to track resource consumption of artificial intelligence operations; and a recommendation engine configured to generate strategies for minimizing environmental impact aligned with sustainability goals.

[0245] In embodiments, the techniques described herein relate to a system, wherein the carbon footprint analytics component is configured to: monitor CO2 emissions associated with computational operations; calculate a total carbon footprint for a specified time period; and generate environmental impact reports.

[0246] In embodiments, the techniques described herein relate to a system, wherein the computational resource tracking component is configured to track at least one of: processing power consumption, energy usage, and infrastructure utilization.

[0247] In embodiments, the techniques described herein relate to a system, wherein the recommendation engine is configmed to: analyze patterns of resource consumption; identify opportunities for resource optimization; and generate recommendations for reducing carbon footprint.

[0248] In embodiments, the techniques described herein relate to a system, further including a visualization component configured to present carbon footprint data and environmental impact metrics through a user interface.Atty Dkt: 11016-8049PCT

[0249] In embodiments, the techniques described herein relate to a system, wherein the recommendation engine is configured to suggest at least one of: selection of more energy efficient artificial intelligence models; timing of operations to utilize renewable energy sources; and optimization of computational processes to reduce resource consumption.

[0250] In embodiments, the techniques described herein relate to a system, further including a comparison component configured to compare environmental impact of different artificial intelligence models.

[0251] In embodiments, the techniques described herein relate to a system, wherein the system is configured to enable selection of artificial intelligence models based at least in part on environmental impact metrics.

[0252] In embodiments, the techniques described herein relate to a system, further including a sustainability reporting component configured to generate reports suitable for environmental compliance and corporate sustainability initiatives.

[0253] In embodiments, the techniques described herein relate to a system, wherein the carbon footprint analytics component is configured to track environmental impact on a per-query basis.

[0254] In embodiments, the techniques described herein relate to a method for environmental monitoring in an artificial intelligence system, the method including: determining, by a carbon footprint analytics component, environmental impact analytics for artificial intelligence operations, wherein the environmental impact analytics include monitoring and analyzing CO2 emissions; tracking, by a computational resource tracking component, resource consumption of artificial intelligence operations; and generating, by a recommendation engine, strategies for minimizing environmental impact aligned with sustainability goals.

[0255] In embodiments, the techniques described herein relate to a method, wherein determining environmental impact analytics includes: monitoring CO2 emissions associated with computational operations; calculating a total carbon footprint for a specified time period; and generating environmental impact reports.

[0256] In embodiments, the techniques described herein relate to a method, wherein tracking resource consumption includes tracking at least one of: processing power consumption, energy usage, and infrastructure utilization.

[0257] In embodiments, the techniques described herein relate to a method, wherein generating strategies includes: analyzing patterns of resource consumption; identifying opportunities for resource optimization; and generating recommendations for reducing carbon footprint.

[0258] In embodiments, the techniques described herein relate to a method, further including presenting carbon footprint data and environmental impact metrics through a user interface.

[0259] In embodiments, the techniques described herein relate to a method, wherein generating strategies includes suggesting at least one of: selection of more energy -efficient artificial intelligence models; timing of operations to utilize renewable energy sources; and optimization of computational processes to reduce resource consumption.

[0260] In embodiments, the techniques described herein relate to a method, further including comparing environmental impact of different artificial intelligence models to enable environmentally -informed model selection.

[0261] In embodiments, the techniques described herein relate to a method, further including enabling selection of artificial intelligence models based at least in part on environmental impact metrics.

[0262] In embodiments, the techniques described herein relate to a method, further including generating reports suitable for environmental compliance and corporate sustainability initiatives.

[0263] In embodiments, the techniques described herein relate to a method, further including tracking environmental impact on a per-query basis to enable detailed environmental accounting.Atty Dkt: 11016-8049PCT

[0264] In embodiments, the techniques described herein relate to a system for providing model improvement analytics associated with artificial intelligence model unification, the system including: a computing platform configured to provide access to a plurality of artificial intelligence models; an analytics subsystem configured to generate analytic measures related to queries processed by the plurality of artificial intelligence models; and a reporting subsystem configured to generate reports including performance metrics associated with the plurality of artificial intelligence models, wherein the performance metrics include at least one of response times, usage patterns, or accuracy indicators.

[0265] In embodiments, the techniques described herein relate to a system, wherein the analytic measures include at least one of query analysis data, performance evaluation data, hallucination metrics, or sustainability metrics.

[0266] In embodiments, the techniques described herein relate to a system, wherein the reporting subsystem is further configured to generate comparative assessments between different artificial intelligence models of the plurality of artificial intelligence models.

[0267] In embodiments, the techniques described herein relate to a system, wherein the comparative assessments include explanations of why one artificial intelligence model outperforms another artificial intelligence model.

[0268] In embodiments, the techniques described herein relate to a system, wherein the performance metrics are provided in real-time as queries are processed.

[0269] In embodiments, the techniques described herein relate to a system, further including a feedback subsystem configmed to provide evaluative information to model developers based on the analytic measures.

[0270] In embodiments, the techniques described herein relate to a system, wherein the evaluative information includes detailed data about query types for which each artificial intelligence model performs well and query types for which each artificial intelligence model falls behind in rankings.

[0271] In embodiments, the techniques described herein relate to a system, wherein the analytics subsystem is further configmed to generate recommendations for product development based on the analytic measures.

[0272] In embodiments, the techniques described herein relate to a system, wherein the reporting subsystem is configmed to enforce model competition by providing comparative performance data to users.

[0273] In embodiments, the techniques described herein relate to a system, wherein the performance metrics include cost metrics associated with processing queries using each artificial intelligence model of the plurality of artificial intelligence models.

[0274] In embodiments, the techniques described herein relate to a method for providing model improvement analytics associated with artificial intelligence model unification, the method including: providing, by a computing platform, access to a plurality of artificial intelligence models; generating, by an analytics subsystem, analytic measures related to queries processed by the plurality of artificial intelligence models; and generating, by a reporting subsystem, reports including performance metrics associated with the plurality of artificial intelligence models, wherein the performance metrics include at least one of response times, usage patterns, or accuracy indicators.

[0275] In embodiments, the techniques described herein relate to a method, wherein generating analytic measures includes determining at least one of query analysis data, performance evaluation data, hallucination metrics, or sustainability metrics.

[0276] In embodiments, the techniques described herein relate to a method, further including generating comparative assessments between different artificial intelligence models of the plurality of artificial intelligence models.Atty Dkt: 11016-8049PCT

[0277] In embodiments, the techniques described herein relate to a method, wherein generating comparative assessments includes providing explanations of why one artificial intelligence model outperforms another artificial intelligence model.

[0278] In embodiments, the techniques described herein relate to a method, wherein the performance metrics are generated at a same time as processing of queries.

[0279] In embodiments, the techniques described herein relate to a method, further including providing evaluative information to model developers based on the analytic measures.

[0280] In embodiments, the techniques described herein relate to a method, wherein providing evaluative information includes providing detailed data about query types for which each artificial intelligence model performs well and query types for which each artificial intelligence model falls behind in rankings.

[0281] In embodiments, the techniques described herein relate to a method, further including generating recommendations for product development based on the analytic measures.

[0282] In embodiments, the techniques described herein relate to a method, further including enforcing model competition by providing comparative performance data to users.

[0283] In embodiments, the techniques described herein relate to a method, wherein the performance metrics include cost metrics associated with processing queries using each artificial intelligence model of the plurality of artificial intelligence models.

[0284] In embodiments, the techniques described herein relate to a system for testing artificial intelligence model configurations, the system including: a computing platform configured to provide access to a plurality of artificial intelligence models; a controlled execution space configured to enable users to validate at least one of input text, system specifications, or system interfaces before operational deployment; and a deployment subsystem configured to migrate validated configurations from the controlled execution space to a production environment.

[0285] In embodiments, the techniques described herein relate to a system, wherein the controlled execution space is isolated from the production environment.

[0286] In embodiments, the techniques described herein relate to a system, wherein the controlled execution space is configured to enable testing of natural language inputs without affecting operational systems.

[0287] In embodiments, the techniques described herein relate to a system, wherein the controlled execution space is configured to enable testing of operational parameters before deployment.

[0288] In embodiments, the techniques described herein relate to a system, wherein the controlled execution space is configured to enable testing of API connections before deployment.

[0289] In embodiments, the techniques described herein relate to a system, wherein the deployment subsystem is configured to prevent deployment unless validation in the controlled execution space is successful.

[0290] In embodiments, the techniques described herein relate to a system, wherein the controlled execution space provides a simulation environment that replicates operational conditions.

[0291] In embodiments, the techniques described herein relate to a system, wherein the controlled execution space is configmed to test interactions between multiple artificial intelligence models.

[0292] In embodiments, the techniques described herein relate to a system, further including a validation subsystem configured to identify potential issues before operational deployment.

[0293] In embodiments, the techniques described herein relate to a system, wherein the controlled execution space enables users to test modifications to input text processing without impacting production systems.Atty Dkt: 11016-8049PCT

[0294] In embodiments, the techniques described herein relate to a method for testing artificial intelligence model configurations, the method including: providing, by a computing platform, access to a plurality of artificial intelligence models; enabling, by a controlled execution space, users to validate at least one of input text, system specifications, or system interfaces before operational deployment; and migrating, by a deployment subsystem, validated configurations from the controlled execution space to a production environment.

[0295] In embodiments, the techniques described herein relate to a method, wherein the controlled execution space is isolated from the production environment.

[0296] In embodiments, the techniques described herein relate to a method, wherein enabling users to validate includes enabling testing of natural language inputs without affecting operational systems.

[0297] In embodiments, the techniques described herein relate to a method, wherein enabling users to validate includes enabling testing of operational parameters before deployment.

[0298] In embodiments, the techniques described herein relate to a method, wherein enabling users to validate includes enabling testing of API connections before deployment.

[0299] In embodiments, the techniques described herein relate to a method, further including preventing deployment unless validation in the controlled execution space is successful.

[0300] In embodiments, the techniques described herein relate to a method, wherein the controlled execution space provides a simulation environment that replicates operational conditions.

[0301] In embodiments, the techniques described herein relate to a method, wherein enabling users to validate includes testing interactions between multiple artificial intelligence models.

[0302] In embodiments, the techniques described herein relate to a method, further including identifying potential issues before operational deployment.

[0303] In embodiments, the techniques described herein relate to a method, wherein enabling users to validate includes testing modifications to input text processing without impacting production systems.

[0304] In embodiments, the techniques described herein relate to a system for facilitating multi-user interaction associated with artificial intelligence model unification, the system including: a computing platform configured to provide access to a plurality of artificial intelligence models; a set of multi-user interfaces associated with artificial intelligence model unification and access, wherein the multi-user interfaces enable multiple users to jointly work on projects; a set of content repositories associated with artificial intelligence model unification and access, wherein the content repositories store reusable natural language inputs; and a change tracking system associated with artificial intelligence model unification and access, wherein the change tracking system maintains revision history for configurations.

[0305] In embodiments, the techniques described herein relate to a system, wherein the multi-user interfaces enable simultaneous access by multiple users to shared project data.

[0306] In embodiments, the techniques described herein relate to a system, wherein the content repositories store template natural language inputs that can be reused across multiple projects.

[0307] In embodiments, the techniques described herein relate to a system, wherein the change tracking system maintains a complete history of modifications to natural language inputs.

[0308] In embodiments, the techniques described herein relate to a system, wherein the change tracking system enables users to revert to previous versions of configurations.Atty Dkt: 11016-8049PCT

[0309] In embodiments, the techniques described herein relate to a system, wherein the multi-user interfaces include access controls that define permissions for different users.

[0310] In embodiments, the techniques described herein relate to a system, wherein the content repositories are organized by project, user, or subject matter category.

[0311] In embodiments, the techniques described herein relate to a system, wherein the change tracking system maintains metadata indicating which user made each modification and when the modification was made.

[0312] In embodiments, the techniques described herein relate to a system, further including a notification subsystem configured to alert users when other users modify shared configurations.

[0313] In embodiments, the techniques described herein relate to a system, wherein the multi-user interfaces enable team based workflow management for complex projects involving multiple artificial intelligence models.

[0314] In embodiments, the techniques described herein relate to a method for facilitating multi-user interaction associated with artificial intelligence model unification, the method including: providing, by a computing platform, access to a plurality of artificial intelligence models; enabling, through multi-user interfaces, multiple users to jointly work on projects associated with artificial intelligence model unification and access; storing, in content repositories, reusable natural language inputs associated with artificial intelligence model unification and access; and maintaining, by a change tracking system, revision history for configurations associated with artificial intelligence model unification and access.

[0315] In embodiments, the techniques described herein relate to a method, wherein enabling multiple users to jointly work includes enabling simultaneous access by multiple users to shared project data.

[0316] In embodiments, the techniques described herein relate to a method, wherein storing reusable natural language inputs includes storing template natural language inputs that can be reused across multiple projects.

[0317] In embodiments, the techniques described herein relate to a method, wherein maintaining revision history includes maintaining a complete history of modifications to natural language inputs.

[0318] In embodiments, the techniques described herein relate to a method, further including enabling users to revert to previous versions of configurations.

[0319] In embodiments, the techniques described herein relate to a method, further including defining access controls that specify permissions for different users.

[0320] In embodiments, the techniques described herein relate to a method, wherein storing reusable natural language inputs includes organizing the content repositories by project, user, or subject matter category.

[0321] In embodiments, the techniques described herein relate to a method, wherein maintaining revision history includes maintaining metadata indicating which user made each modification and when the modification was made.

[0322] In embodiments, the techniques described herein relate to a method, further including alerting users when other users modify shared configurations.

[0323] In embodiments, the techniques described herein relate to a method, wherein enabling multiple users to jointly work includes enabling team based workflow management for complex projects involving multiple artificial intelligence models.

[0324] In embodiments, the techniques described herein relate to a system for managing resource consumption associated with artificial intelligence model unification, the system including: a computing platform configured to provide access to a plurality of artificial intelligence models, wherein each artificial intelligence model is associated with a cost per query; a financial control subsystem configmed to: set temporal budget constraints for users; and trackAtty Dkt: 11016-8049PCT cumulative costs incurred by each user during a time period; a quality control subsystem configured to define multiple service levels for model selection, wherein each service level is associated with a different cost range; and a processing subsystem configured to select one or more artificial intelligence models from the plurality of artificial intelligence models based on the service level defined for a user and remaining budget within the temporal budget constraints.

[0325] In embodiments, the techniques described herein relate to a system, wherein the temporal budget constraints include daily allocation limits that reset at predetermined times.

[0326] In embodiments, the techniques described herein relate to a system, wherein the financial control subsystem is further configured to prevent users from submitting additional queries when the daily allocation limit is reached unless the user modifies the limit or activates a manual override.

[0327] In embodiments, the techniques described herein relate to a system, wherein the financial control subsystem is configured to maintain a log recording when manual overrides are activated and which users activated them.

[0328] In embodiments, the techniques described herein relate to a system, wherein the multiple service levels include at least four tiers ranging from low-cost to high cost artificial intelligence models.

[0329] In embodiments, the techniques described herein relate to a system, wherein the four tiers include: a first tier associated with low-cost artificial intelligence models; a second tier associated with economical artificial intelligence models; a third tier associated with moderate-cost artificial intelligence models; and a fourth tier associated with premium artificial intelligence models.

[0330] In embodiments, the techniques described herein relate to a system, wherein the quality control subsystem is further configured to define separate service levels for input processing and output processing.

[0331] In embodiments, the techniques described herein relate to a system, wherein the processing subsystem is configmed to select a first set of artificial intelligence models for input processing based on a first service level and a second set of artificial intelligence models for output processing based on a second service level.

[0332] In embodiments, the techniques described herein relate to a system, wherein the processing subsystem dynamically adjusts model selection based on cumulative costs already incurred to avoid exceeding the temporal budget constraints.

[0333] In embodiments, the techniques described herein relate to a system, further including a reporting subsystem configmed to provide users with transparency regarding costs incurred for each query.

[0334] In embodiments, the techniques described herein relate to a method for managing resource consumption associated with artificial intelligence model unification, the method including: providing, by a computing platform, access to a plurality of artificial intelligence models, wherein each artificial intelligence model is associated with a cost per query; setting, by a financial control subsystem, temporal budget constraints for users; tracking, by the financial control subsystem, cumulative costs incurred by each user during a time period; defining, by a quality control subsystem, multiple service levels for model selection, wherein each service level is associated with a different cost range; and selecting, by a processing subsystem, one or more artificial intelligence models from the plurality of artificial intelligence models based on the service level defined for a user and remaining budget within the temporal budget constraints.

[0335] In embodiments, the techniques described herein relate to a method, wherein the temporal budget constraints include daily allocation limits that reset at predetermined times.Atty Dkt: 11016-8049PCT

[0336] In embodiments, the techniques described herein relate to a method, further including preventing users from submitting additional queries when the daily allocation limit is reached unless the user modifies the limit or activates a manual override.

[0337] In embodiments, the techniques described herein relate to a method, further including maintaining a log recording when manual overrides are activated and which users activated them.

[0338] In embodiments, the techniques described herein relate to a method, wherein the multiple service levels include at least four tiers ranging from low-cost to high cost artificial intelligence models.

[0339] In embodiments, the techniques described herein relate to a method, wherein the four tiers include: a first tier associated with low-cost artificial intelligence models; a second tier associated with economical artificial intelligence models; a third tier associated with moderate-cost artificial intelligence models; and a fourth tier associated with premium artificial intelligence models.

[0340] In embodiments, the techniques described herein relate to a method, wherein defining multiple service levels includes defining separate service levels for input processing and output processing.

[0341] In embodiments, the techniques described herein relate to a method, wherein selecting one or more artificial intelligence models includes selecting a first set of artificial intelligence models for input processing based on a first service level and a second set of artificial intelligence models for output processing based on a second service level.

[0342] In embodiments, the techniques described herein relate to a method, wherein selecting one or more artificial intelligence models includes dynamically adjusting model selection based on cumulative costs already incurred to avoid exceeding the temporal budget constraints.

[0343] In embodiments, the techniques described herein relate to a method, further including providing users with transparency regarding costs incurred for each query. Automation of Response Challenging and Feedback

[0344] In embodiments, the techniques described herein relate to a system for automated output verification associated with artificial intelligence model unification, the system including: a computing platform configured to provide access to a plurality of artificial intelligence models; an input processing subsystem configured to receive a user input and process the user input to generate processed input data; a model selection subsystem configured to select one or more artificial intelligence models from the plurality of artificial intelligence models to process the processed input data; a response generation subsystem configured to generate one or more responses by providing the processed input data to the one or more artificial intelligence models; and a validation subsystem configured to: generate verification inputs based on the user input and the one or more responses; and provide the verification inputs to at least one artificial intelligence model to validate accuracy of the one or more responses.

[0345] In embodiments, the techniques described herein relate to a system, wherein the validation subsystem is configmed to generate the verification inputs immediately after receiving the one or more responses.

[0346] In embodiments, the techniques described herein relate to a system, wherein the verification inputs include challenge questions configured to test whether the one or more responses contain fabricated information.

[0347] In embodiments, the techniques described herein relate to a system, wherein the validation subsystem is configmed to iteratively improve the one or more responses based on outputs from the at least one artificial intelligence model in response to the verification inputs.

[0348] In embodiments, the techniques described herein relate to a system, wherein the input processing subsystem is configured to: decompose the user input into a plurality of component inputs; and determine a sequence for processing the plurality of component inputs.Atty Dkt: 11016-8049PCT

[0349] In embodiments, the techniques described herein relate to a system, wherein the validation subsystem is further configured to generate verification inputs to validate the sequence for processing the plurality of component inputs.

[0350] In embodiments, the techniques described herein relate to a system, wherein the verification inputs for validating the sequence include questions about whether the plurality of component inputs are in proper sequence and whether the plurality of component inputs are comprehensive.

[0351] In embodiments, the techniques described herein relate to a system, wherein the model selection subsystem is configured to select different artificial intelligence models for generating responses and for validating responses.

[0352] In embodiments, the techniques described herein relate to a system, wherein the response generation subsystem is configured to generate multiple responses using multiple artificial intelligence models, and wherein the validation subsystem is configured to rank the multiple responses based on quality criteria.

[0353] In embodiments, the techniques described herein relate to a system, wherein the quality criteria include at least two of accuracy, clarity, or conciseness.

[0354] In embodiments, the techniques described herein relate to a method for automated output verification associated with artificial intelligence model unification, the method including: providing, by a computing platform, access to a plurality of artificial intelligence models; receiving, by an input processing subsystem, a user input; processing, by the input processing subsystem, the user input to generate processed input data; selecting, by a model selection subsystem, one or more artificial intelligence models from the plurality of artificial intelligence models to process the processed input data; generating, by a response generation subsystem, one or more responses by providing the processed input data to the one or more artificial intelligence models; generating, by a validation subsystem, verification inputs based on the user input and the one or more responses; and providing, by the validation subsystem, the verification inputs to at least one artificial intelligence model to validate accuracy of the one or more responses.

[0355] In embodiments, the techniques described herein relate to a method, wherein generating verification inputs occurs immediately after generating the one or more responses.

[0356] In embodiments, the techniques described herein relate to a method, wherein the verification inputs include challenge questions configured to test whether the one or more responses contain fabricated information.

[0357] In embodiments, the techniques described herein relate to a method, further including iteratively improving the one or more responses based on outputs from the at least one artificial intelligence model in response to the verification inputs.

[0358] In embodiments, the techniques described herein relate to a method, wherein processing the user input includes: decomposing the user input into a plurality of component inputs; and determining a sequence for processing the plurality of component inputs.

[0359] In embodiments, the techniques described herein relate to a method, further including generating verification inputs to validate the sequence for processing the plurality of component inputs.

[0360] In embodiments, the techniques described herein relate to a method, wherein the verification inputs for validating the sequence include questions about whether the plurality of component inputs are in proper sequence and whether the plurality of component inputs are comprehensive.

[0361] In embodiments, the techniques described herein relate to a method, wherein selecting one or more artificial intelligence models includes selecting different artificial intelligence models for generating responses and for validating responses.Atty Dkt: 11016-8049PCT

[0362] In embodiments, the techniques described herein relate to a method, wherein generating one or more responses includes generating multiple responses using multiple artificial intelligence models, and further including ranking the multiple responses based on quality criteria.

[0363] In embodiments, the techniques described herein relate to a method, wherein the quality criteria include at least two of accuracy, clarity, or conciseness. Asynchronous Management of Prompts and Responses

[0364] In embodiments, the techniques described herein relate to a system for concurrent processing of multiple inputs associated with artificial intelligence model unification, the system including: a computing platform configured to provide access to a plurality of artificial intelligence models; an input management subsystem configured to receive a plurality of user inputs and manage the plurality of user inputs in a non-blocking manner; a processing subsystem configmed to distribute the plurality of user inputs to the plurality of artificial intelligence models such that multiple user inputs are processed in parallel; and a response management subsystem configured to collect responses from the plurality of artificial intelligence models and associate each response with a corresponding user input without requiring sequential processing.

[0365] In embodiments, the techniques described herein relate to a system, wherein the input management subsystem is configured to queue incoming user inputs and dispatch them to available artificial intelligence models as processing capacity becomes available.

[0366] In embodiments, the techniques described herein relate to a system, wherein the processing subsystem is configmed to send different portions of a single user input to different artificial intelligence models concurrently.

[0367] In embodiments, the techniques described herein relate to a system, wherein the response management subsystem is configmed to return responses to users as they become available rather than waiting for all responses to be generated.

[0368] In embodiments, the techniques described herein relate to a system, further including a coordination subsystem configmed to manage dependencies between related user inputs while allowing independent user inputs to be processed in parallel.

[0369] In embodiments, the techniques described herein relate to a system, wherein the system is configured to handle large-scale deployment involving thousands of concurrent user inputs without compromising performance.

[0370] In embodiments, the techniques described herein relate to a system, wherein the processing subsystem is configmed to dynamically allocate processing resources based on current system load.

[0371] In embodiments, the techniques described herein relate to a system, wherein the input management subsystem is configured to assign priorities to user inputs and process higher priority inputs before lower priority inputs.

[0372] In embodiments, the techniques described herein relate to a system, wherein the response management subsystem is configured to aggregate multiple partial responses into a final response for a user.

[0373] In embodiments, the techniques described herein relate to a system, wherein the system is configured to process decomposed components of a user input in parallel when the components me independent of each other.

[0374] In embodiments, the techniques described herein relate to a method for concurrent processing of multiple inputs associated with artificial intelligence model unification, the method including: providing, by a computing platform, access to a plurality of artificial intelligence models; receiving, by an input management subsystem, a plurality of user inputs; managing, by the input management subsystem, the plurality of user inputs in a non-blocking manner; distributing, by a processing subsystem, the plurality of user inputs to the plurality of artificial intelligence models such that multiple user inputs are processed in parallel; and collecting, by a response management subsystem,Atty Dkt: 11016-8049PCT responses from the plurality of artificial intelligence models and associating each response with a corresponding user input without requiring sequential processing.

[0375] In embodiments, the techniques described herein relate to a method, wherein managing the plurality of user inputs includes queuing incoming user inputs and dispatching them to available artificial intelligence models as processing capacity becomes available.

[0376] In embodiments, the techniques described herein relate to a method, wherein distributing the plurality of user inputs includes sending different portions of a single user input to different artificial intelligence models concurrently.

[0377] In embodiments, the techniques described herein relate to a method, further including returning responses to users as they become available rather than waiting for all responses to be generated.

[0378] In embodiments, the techniques described herein relate to a method, further including managing dependencies between related user inputs while allowing independent user inputs to be processed in parallel.

[0379] In embodiments, the techniques described herein relate to a method, further including handling large-scale deployment involving thousands of concurrent user inputs without compromising performance.

[0380] In embodiments, the techniques described herein relate to a method, further including dynamically allocating processing resources based on current system load.

[0381] In embodiments, the techniques described herein relate to a method, wherein managing the plurality of user inputs includes assigning priorities to user inputs and processing higher priority inputs before lower priority inputs.

[0382] In embodiments, the techniques described herein relate to a method, further including aggregating multiple partial responses into a final response for a user.

[0383] In embodiments, the techniques described herein relate to a method, further including processing decomposed components of a user input in parallel when the components are independent of each other. Operational, Strategic, Decision, and Supervised Autonomy Workflows

[0384] In embodiments, the techniques described herein relate to a system for managing automated workflows associated with artificial intelligence model unification, the system including: a computing platform configured to provide access to a plurality of artificial intelligence models; a workflow definition subsystem configured to enable users to design process sequences involving one or more artificial intelligence models; a workflow configuration subsystem configmed to set operational parameters for the process sequences; a workflow execution subsystem configured to automatically execute the process sequences based on triggering events; and a workflow tracking subsystem configmed to monitor execution of the process sequences and record workflow states.

[0385] In embodiments, the techniques described herein relate to a system, wherein the process sequences include at least one of operational workflows, strategic workflows, decision workflows, or supervised autonomy workflows.

[0386] In embodiments, the techniques described herein relate to a system, wherein supervised autonomy workflows include workflows that include human approval steps at predetermined points during automated execution.

[0387] In embodiments, the techniques described herein relate to a system, wherein the workflow definition subsystem is configured to enable users to specify conditional branching based on outputs from artificial intelligence models.

[0388] In embodiments, the techniques described herein relate to a system, wherein the workflow execution subsystem is configured to execute workflows in response to scheduled triggers or event based triggers.

[0389] In embodiments, the techniques described herein relate to a system, wherein the workflow tracking subsystem is configured to generate alerts when workflows encounter errors or require human intervention.Atty Dkt: 11016-8049PCT

[0390] In embodiments, the techniques described herein relate to a system, wherein the workflow configuration subsystem is configured to set resource allocation limits for workflows.

[0391] In embodiments, the techniques described herein relate to a system, wherein the workflow definition subsystem enables users to define multi-step workflows that involve sequential processing by multiple different artificial intelligence models.

[0392] In embodiments, the techniques described herein relate to a system, further including a workflow optimization subsystem configmed to analyze workflow performance and recommend improvements.

[0393] In embodiments, the techniques described herein relate to a system, wherein the workflow execution subsystem is configured to handle both synchronous workflows and asynchronous workflows.

[0394] In embodiments, the techniques described herein relate to a method for managing automated workflows associated with artificial intelligence model unification, the method including: providing, by a computing platform, access to a plurality of artificial intelligence models; enabling, by a workflow definition subsystem, users to design process sequences involving one or more artificial intelligence models; setting, by a workflow configuration subsystem, operational parameters for the process sequences; automatically executing, by a workflow execution subsystem, the process sequences based on triggering events; and monitoring, by a workflow tracking subsystem, execution of the process sequences and recording workflow states.

[0395] In embodiments, the techniques described herein relate to a method, wherein the process sequences include at least one of operational workflows, strategic workflows, decision workflows, or supervised autonomy workflows.

[0396] In embodiments, the techniques described herein relate to a method, wherein supervised autonomy workflows include workflows that include human approval steps at predetermined points during automated execution.

[0397] In embodiments, the techniques described herein relate to a method, wherein enabling users to design process sequences includes enabling users to specify conditional branching based on outputs from artificial intelligence models.

[0398] In embodiments, the techniques described herein relate to a method, wherein automatically executing the process sequences includes executing workflows in response to scheduled triggers or event based triggers.

[0399] In embodiments, the techniques described herein relate to a method, further including generating alerts when workflows encounter errors or require human intervention.

[0400] In embodiments, the techniques described herein relate to a method, wherein setting operational parameters includes setting resource allocation limits for workflows.

[0401] In embodiments, the techniques described herein relate to a method, wherein enabling users to design process sequences includes enabling users to define multi-step workflows that involve sequential processing by multiple different artificial intelligence models.

[0402] In embodiments, the techniques described herein relate to a method, further including analyzing workflow performance and recommending improvements.

[0403] In embodiments, the techniques described herein relate to a method, wherein automatically executing the process sequences includes handling both synchronous workflows and asynchronous workflows. Prompt Pre- Processing and Metadata Calculation

[0404] In embodiments, the techniques described herein relate to a system for input transformation associated with artificial intelligence model unification, the system including: a computing platform configured to provide access to a plurality of artificial intelligence models; a metadata extraction subsystem configured to analyze a user input andAtty Dkt: 11016-8049PCT extract descriptive data including at least one of semantic indicators, temporal characteristics, or processing characteristics; a complexity evaluation subsystem configmed to determine a difficulty level of the user input based on the descriptive data; a settings determination subsystem configured to select processing parameters for the user input based on the difficulty level; and an input processing subsystem configured to process the user input using the processing parameters.

[0405] In embodiments, the techniques described herein relate to a system, wherein the semantic indicators include keywords extracted from the user input.

[0406] In embodiments, the techniques described herein relate to a system, wherein the temporal characteristics include an indication of whether the user input relates to past events, present events, or future events.

[0407] In embodiments, the techniques described herein relate to a system, wherein the processing characteristics include a complexity score on a numerical scale.

[0408] In embodiments, the techniques described herein relate to a system, wherein the complexity score ranges from 1 to 5, with 1 representing low difficulty and 5 representing high difficulty.

[0409] In embodiments, the techniques described herein relate to a system, wherein the settings determination subsystem is configured to select at least one of: whether to decompose the user input, which artificial intelligence models to use, or how many artificial intelligence models to use.

[0410] In embodiments, the techniques described herein relate to a system, further including a user interaction subsystem configured to query the user for additional information when the descriptive data indicates ambiguity in the user input.

[0411] In embodiments, the techniques described herein relate to a system, further including a redundancy detection subsystem configured to identify repetitive content in the user input and modify the user input to remove the repetitive content.

[0412] In embodiments, the techniques described herein relate to a system, further including a data retrieval subsystem configmed to retrieve contextual information relevant to the user input from external data sources.

[0413] In embodiments, the techniques described herein relate to a system, wherein the metadata extraction subsystem is configured to identify whether the user input includes data to be processed, questions to be answered, or both.

[0414] In embodiments, the techniques described herein relate to a method for input transformation associated with artificial intelligence model unification, the method including: providing, by a computing platform, access to a plurality of artificial intelligence models; analyzing, by a metadata extraction subsystem, a user input; extracting, by the metadata extraction subsystem, descriptive data including at least one of semantic indicators, temporal characteristics, or processing characteristics; determining, by a complexity evaluation subsystem, a difficulty level of the user input based on the descriptive data; selecting, by a settings determination subsystem, processing parameters for the user input based on the difficulty level; and processing, by an input processing subsystem, the user input using the processing parameters.

[0415] In embodiments, the techniques described herein relate to a method, wherein the semantic indicators include keywords extracted from the user input.

[0416] In embodiments, the techniques described herein relate to a method, wherein the temporal characteristics include an indication of whether the user input relates to past events, present events, or future events.

[0417] In embodiments, the techniques described herein relate to a method, wherein the processing characteristics include a complexity score on a numerical scale.Atty Dkt: 11016-8049PCT

[0418] In embodiments, the techniques described herein relate to a method, wherein the complexity score ranges from 1 to 5, with 1 representing low difficulty and 5 representing high difficulty.

[0419] In embodiments, the techniques described herein relate to a method, wherein selecting processing parameters includes selecting at least one of: whether to decompose the user input, which artificial intelligence models to use, or how many artificial intelligence models to use.

[0420] In embodiments, the techniques described herein relate to a method, further including querying the user for additional information when the descriptive data indicates ambiguity in the user input.

[0421] In embodiments, the techniques described herein relate to a method, further including identifying repetitive content in the user input and modifying the user input to remove the repetitive content.

[0422] In embodiments, the techniques described herein relate to a method, further including retrieving contextual information relevant to the user input from external data sources.

[0423] In embodiments, the techniques described herein relate to a method, wherein extracting descriptive data includes identifying whether the user input includes data to be processed, questions to be answered, or both. Settings Matrix and Processing Settings

[0424] In embodiments, the techniques described herein relate to a system for dynamic configuration selection associated with artificial intelligence model unification, the system including: a computing platform configured to provide access to a plurality of artificial intelligence models; an input analysis subsystem configmed to determine characteristics of a user input; a configuration mapping subsystem including a decision framework that maps combinations of input characteristics and user defined preferences to processing configurations; and a processing subsystem configured to process the user input according to a processing configuration selected from the decision framework based on the characteristics of the user input and the user defined preferences.

[0425] In embodiments, the techniques described herein relate to a system, wherein the input characteristics include a difficulty level of the user input.

[0426] In embodiments, the techniques described herein relate to a system, wherein the user defined preferences include a cost tier selection indicating a preferred balance between cost and quality.

[0427] In embodiments, the techniques described herein relate to a system, wherein the user defined preferences include a processing sophistication setting indicating a level of processing complexity to apply.

[0428] In embodiments, the techniques described herein relate to a system, wherein the processing configuration includes settings for at least one of: whether to decompose the user input, whether to validate outputs, which artificial intelligence models to use, or how many artificial intelligence models to use.

[0429] In embodiments, the techniques described herein relate to a system, wherein the decision framework includes a matrix with multiple dimensions including input difficulty level, cost tier, and processing sophistication level.

[0430] In embodiments, the techniques described herein relate to a system, wherein the processing configuration includes separate settings for input processing and output processing.

[0431] In embodiments, the techniques described herein relate to a system, wherein the configuration mapping subsystem uses a first algorithm to determine input processing settings and a second algorithm to determine output processing settings.

[0432] In embodiments, the techniques described herein relate to a system, wherein the decision framework is configmed to prioritize cost constraints over quality when temporal budget limits are being approached.Atty Dkt: 11016-8049PCT

[0433] In embodiments, the techniques described herein relate to a system, further including a learning subsystem configured to update the decision framework based on historical performance data.

[0434] In embodiments, the techniques described herein relate to a method for dynamic configuration selection associated with artificial intelligence model unification, the method including: providing, by a computing platform, access to a plurality of artificial intelligence models; determining, by an input analysis subsystem, characteristics of a user input; accessing, by a configuration mapping subsystem, a decision framework that maps combinations of input characteristics and user defined preferences to processing configurations; selecting, by the configuration mapping subsystem, a processing configuration from the decision framework based on the characteristics of the user input and the user defined preferences; and processing, by a processing subsystem, the user input according to the selected processing configuration.

[0435] In embodiments, the techniques described herein relate to a method, wherein the input characteristics include a difficulty level of the user input.

[0436] In embodiments, the techniques described herein relate to a method, wherein the user defined preferences include a cost tier selection indicating a preferred balance between cost and quality.

[0437] In embodiments, the techniques described herein relate to a method, wherein the user defined preferences include a processing sophistication setting indicating a level of processing complexity to apply.

[0438] In embodiments, the techniques described herein relate to a method, wherein the processing configuration includes settings for at least one of: whether to decompose the user input, whether to validate outputs, which artificial intelligence models to use, or how many artificial intelligence models to use.

[0439] In embodiments, the techniques described herein relate to a method, wherein the decision framework includes a matrix with multiple dimensions including input difficulty level, cost tier, and processing sophistication level.

[0440] In embodiments, the techniques described herein relate to a method, wherein the processing configuration includes separate settings for input processing and output processing.

[0441] In embodiments, the techniques described herein relate to a method, further including using a first algorithm to determine input processing settings and a second algorithm to determine output processing settings.

[0442] In embodiments, the techniques described herein relate to a method, further including prioritizing cost constraints over quality when temporal budget limits are being approached.

[0443] In embodiments, the techniques described herein relate to a method, further including updating the decision framework based on historical performance data. Prompt Decomposition, Chaining, and Consensus Ranking

[0444] In embodiments, the techniques described herein relate to a system for query decomposition and sequential processing associated with artificial intelligence model unification, the system including: a computing platform configmed to provide access to a plurality of artificial intelligence models; a decomposition subsystem configured to: analyze a user input to identify multiple component tasks; and generate a plurality of component inputs, each component input corresponding to one of the multiple component tasks; a sequencing subsystem configured to determine a processing order for the plurality of component inputs based on dependencies between the component tasks; a processing subsystem configured to process the plurality of component inputs according to the processing order using one or more artificial intelligence models; and an aggregation subsystem configured to combine outputs from processing the plurality of component inputs into a final output.Atty Dkt: 11016-8049PCT

[0445] In embodiments, the techniques described herein relate to a system, wherein the decomposition subsystem is configmed to use at least one artificial intelligence model from the plurality of artificial intelligence models to identify the multiple component tasks.

[0446] In embodiments, the techniques described herein relate to a system, wherein the sequencing subsystem is configured to determine whether component inputs should be processed sequentially or in parallel based on whether dependencies exist between them.

[0447] In embodiments, the techniques described herein relate to a system, further including a validation subsystem configured to verify that the plurality of component inputs collectively address all aspects of the user input.

[0448] In embodiments, the techniques described herein relate to a system, wherein the validation subsystem is configured to generate verification inputs that ask whether the plurality of component inputs are in proper sequence and whether they are comprehensive.

[0449] In embodiments, the techniques described herein relate to a system, wherein the processing subsystem is configured to send multiple component inputs to a single artificial intelligence model as a bundled input to reduce API calls.

[0450] In embodiments, the techniques described herein relate to a system, wherein the processing subsystem is configmed to send different component inputs to different artificial intelligence models based on model specializations.

[0451] In embodiments, the techniques described herein relate to a system, further including a consensus subsystem configmed to: generate multiple alternative decompositions of the user input using multiple artificial intelligence models; evaluate quality of each alternative decomposition; and select a highest-quality decomposition for processing.

[0452] In embodiments, the techniques described herein relate to a system, wherein evaluating quality includes using the multiple artificial intelligence models to rank the alternative decompositions.

[0453] In embodiments, the techniques described herein relate to a system, wherein the decomposition subsystem is configured to break down complex user inputs that exceed context window limits of individual artificial intelligence models.

[0454] In embodiments, the techniques described herein relate to a method for query decomposition and sequential processing associated with artificial intelligence model unification, the method including: providing, by a computing platform, access to a plurality of artificial intelligence models; analyzing, by a decomposition subsystem, a user input to identify multiple component tasks; generating, by the decomposition subsystem, a plurality of component inputs, each component input corresponding to one of the multiple component tasks; determining, by a sequencing subsystem, a processing order for the plurality of component inputs based on dependencies between the component tasks; processing, by a processing subsystem, the plurality of component inputs according to the processing order using one or more artificial intelligence models; and combining, by an aggregation subsystem, outputs from processing the plurality of component inputs into a final output.

[0455] In embodiments, the techniques described herein relate to a method, wherein analyzing the user input includes using at least one artificial intelligence model from the plurality of artificial intelligence models to identify the multiple component tasks.

[0456] In embodiments, the techniques described herein relate to a method, wherein determining the processing order includes determining whether component inputs should be processed sequentially or in parallel based on whether dependencies exist between them.Atty Dkt: 11016-8049PCT

[0457] In embodiments, the techniques described herein relate to a method, further including verifying that the plurality of component inputs collectively address all aspects of the user input.

[0458] In embodiments, the techniques described herein relate to a method, wherein verifying includes generating verification inputs that ask whether the plurality of component inputs are in proper sequence and whether they are comprehensive.

[0459] In embodiments, the techniques described herein relate to a method, wherein processing the plurality of component inputs includes sending multiple component inputs to a single artificial intelligence model as a bundled input to reduce API calls.

[0460] In embodiments, the techniques described herein relate to a method, wherein processing the plurality of component inputs includes sending different component inputs to different artificial intelligence models based on model specializations.

[0461] In embodiments, the techniques described herein relate to a method, further including: generating multiple alternative decompositions of the user input using multiple artificial intelligence models; evaluating quality of each alternative decomposition; and selecting a highest-quality decomposition for processing.

[0462] In embodiments, the techniques described herein relate to a method, wherein evaluating quality includes using the multiple artificial intelligence models to rank the alternative decompositions.

[0463] In embodiments, the techniques described herein relate to a method, wherein analyzing the user input includes breaking down complex user inputs that exceed context window limits of individual artificial intelligence models. Model Smart Router (MSR) Processing

[0464] In embodiments, the techniques described herein relate to a system for intelligent model selection associated with artificial intelligence model unification, the system including: a computing platform configured to provide access to a plurality of artificial intelligence models; a performance tracking subsystem configured to maintain historical performance data for the plurality of artificial intelligence models across different query types; a routing subsystem configured to: analyze characteristics of a user input; identify one or more artificial intelligence models from the plurality of artificial intelligence models that are predicted to perform optimally for the user input based on the historical performance data and the characteristics of the user input; and direct the user input to the identified one or more artificial intelligence models; and a feedback subsystem configured to update the historical performance data based on quality of outputs generated in response to the user input.

[0465] In embodiments, the techniques described herein relate to a system, wherein the characteristics of the user input include at least one of: subject matter, temporal context, geographic context, or complexity level.

[0466] In embodiments, the techniques described herein relate to a system, wherein the routing subsystem is configured to select different artificial intelligence models for different users based on user-specific preferences or permissions.

[0467] In embodiments, the techniques described herein relate to a system, wherein the routing subsystem is configmed to dynamically switch between artificial intelligence models during processing of a single user input based on intermediate results.

[0468] In embodiments, the techniques described herein relate to a system, further including a caching subsystem configmed to store responses to repetitive user inputs to avoid redundant processing.

[0469] In embodiments, the techniques described herein relate to a system, further including a context management subsystem configmed to maintain contextual information across multiple sequential user inputs from a single user.Atty Dkt: 11016-8049PCT

[0470] In embodiments, the techniques described herein relate to a system, wherein the context management subsystem is configured to store contextual information that exceeds context window limitations of individual artificial intelligence models.

[0471] In embodiments, the techniques described herein relate to a system, wherein the routing subsystem is configured to consider cost constraints when identifying the one or more artificial intelligence models.

[0472] In embodiments, the techniques described herein relate to a system, wherein the routing subsystem is configured to send the user input to multiple artificial intelligence models in parallel and select a best output based on evaluation criteria.

[0473] In embodiments, the techniques described herein relate to a system, wherein the feedback subsystem is configured to use automated consensus ranking to determine quality scores for outputs.

[0474] In embodiments, the techniques described herein relate to a method for intelligent model selection associated with artificial intelligence model unification, the method including: providing, by a computing platform, access to a plurality of artificial intelligence models; maintaining, by a performance tracking subsystem, historical performance data for the plurality of artificial intelligence models across different query types; analyzing, by a routing subsystem, characteristics of a user input; identifying, by the routing subsystem, one or more artificial intelligence models from the plurality of artificial intelligence models that are predicted to perform optimally for the user input based on the historical performance data and the characteristics of the user input; directing, by the routing subsystem, the user input to the identified one or more artificial intelligence models; and updating, by a feedback subsystem, the historical performance data based on quality of outputs generated in response to the user input.

[0475] In embodiments, the techniques described herein relate to a method, wherein the characteristics of the user input include at least one of: subject matter, temporal context, geographic context, or complexity level.

[0476] In embodiments, the techniques described herein relate to a method, wherein identifying one or more artificial intelligence models includes selecting different artificial intelligence models for different users based on user-specific preferences or permissions.

[0477] In embodiments, the techniques described herein relate to a method, further including dynamically switching between artificial intelligence models during processing of a single user input based on intermediate results.

[0478] In embodiments, the techniques described herein relate to a method, further including storing responses to repetitive user inputs to avoid redundant processing.

[0479] In embodiments, the techniques described herein relate to a method, further including maintaining contextual information across multiple sequential user inputs from a single user.

[0480] In embodiments, the techniques described herein relate to a method, wherein maintaining contextual information includes storing contextual information that exceeds context window limitations of individual artificial intelligence models.

[0481] In embodiments, the techniques described herein relate to a method, wherein identifying one or more artificial intelligence models includes considering cost constraints.

[0482] In embodiments, the techniques described herein relate to a method, further including sending the user input to multiple artificial intelligence models in parallel and selecting a best output based on evaluation criteria.

[0483] In embodiments, the techniques described herein relate to a method, wherein updating the historical performance data includes using automated consensus ranking to determine quality scores for outputs. Extract, Iterate, Check OperationsAtty Dkt: 11016-8049PCT

[0484] In embodiments, the techniques described herein relate to a system for multi-model processing with quality control associated with artificial intelligence model unification, the system including: a computing platform configured to provide access to a plurality of artificial intelligence models; a model selection subsystem configured to extract a subset of artificial intelligence models from the plurality of artificial intelligence models based on selection criteria; an iteration subsystem configured to send a user input to each artificial intelligence model in the subset to generate multiple outputs; a validation subsystem configured to determine whether to apply automated verification to the multiple outputs based on configuration settings; and a ranking subsystem configured to determine whether to apply consensus ranking to the multiple outputs based on the configuration settings.

[0485] In embodiments, the techniques described herein relate to a system, wherein the selection criteria include historical performance rankings for different query types.

[0486] In embodiments, the techniques described herein relate to a system, wherein the selection criteria include cost tier settings that limit selection to artificial intelligence models within a specified cost range.

[0487] In embodiments, the techniques described herein relate to a system, wherein the validation subsystem is configured to generate verification inputs for each output when automated verification is enabled.

[0488] In embodiments, the techniques described herein relate to a system, wherein the validation subsystem is configured to iteratively improve outputs based on responses to the verification inputs.

[0489] In embodiments, the techniques described herein relate to a system, wherein the ranking subsystem is configured to send each output to each artificial intelligence model in the subset with instructions to rank all outputs when consensus ranking is enabled.

[0490] In embodiments, the techniques described herein relate to a system, wherein the ranking subsystem is configured to calculate weighted scores based on rankings from all artificial intelligence models in the subset.

[0491] In embodiments, the techniques described herein relate to a system, wherein the ranking subsystem is configured to select an output with a highest weighted score as a final output.

[0492] In embodiments, the techniques described herein relate to a system, wherein the configuration settings are determined dynamically based on at least one of: complexity of the user input, available budget, or user preferences.

[0493] In embodiments, the techniques described herein relate to a system, wherein the iteration subsystem is configured to process artificial intelligence models in the subset sequentially or in parallel based on system configuration.

[0494] In embodiments, the techniques described herein relate to a method for multi-model processing with quality control associated with artificial intelligence model unification, the method including: providing, by a computing platform, access to a plurality of artificial intelligence models; extracting, by a model selection subsystem, a subset of artificial intelligence models from the plurality of artificial intelligence models based on selection criteria; sending, by an iteration subsystem, a user input to each artificial intelligence model in the subset to generate multiple outputs; determining, by a validation subsystem, whether to apply automated verification to the multiple outputs based on configuration settings; and determining, by a ranking subsystem, whether to apply consensus ranking to the multiple outputs based on the configuration settings.

[0495] In embodiments, the techniques described herein relate to a method, wherein the selection criteria include historical performance rankings for different query types.

[0496] In embodiments, the techniques described herein relate to a method, wherein the selection criteria include cost tier settings that limit selection to artificial intelligence models within a specified cost range.Atty Dkt: 11016-8049PCT

[0497] In embodiments, the techniques described herein relate to a method, further including generating verification inputs for each output when automated verification is enabled.

[0498] In embodiments, the techniques described herein relate to a method, further including iteratively improving outputs based on responses to the verification inputs.

[0499] In embodiments, the techniques described herein relate to a method, further including sending each output to each artificial intelligence model in the subset with instructions to rank all outputs when consensus ranking is enabled.

[0500] In embodiments, the techniques described herein relate to a method, further including calculating weighted scores based on rankings from all artificial intelligence models in the subset.

[0501] In embodiments, the techniques described herein relate to a method, further including selecting an output with a highest weighted score as a final output.

[0502] In embodiments, the techniques described herein relate to a method, wherein the configmation settings are determined dynamically based on at least one of: complexity of the user input, available budget, or user preferences.

[0503] In embodiments, the techniques described herein relate to a method, wherein sending the user input to each artificial intelligence model includes processing artificial intelligence models in the subset sequentially or in parallel based on system configuration. Response Model Consensus Ranking (RMCR)

[0504] In embodiments, the techniques described herein relate to a system for consensus based output evaluation associated with artificial intelligence model unification, the system including: a computing platform configured to provide access to a plurality of artificial intelligence models; a response generation subsystem configured to generate multiple candidate outputs by providing a user input to multiple artificial intelligence models from the plurality of artificial intelligence models; an evaluation subsystem configured to: send each candidate output to each of the multiple artificial intelligence models along with instructions to evaluate all candidate outputs; receive evaluation data from each of the multiple artificial intelligence models, wherein the evaluation data includes rankings or scores for each candidate output; and a selection subsystem configmed to: calculate aggregate evaluation scores for each candidate output based on the evaluation data from all of the multiple artificial intelligence models; and select a final output from the candidate outputs based on the aggregate evaluation scores.

[0505] In embodiments, the techniques described herein relate to a system, wherein the instructions to evaluate specify evaluation criteria including at least two of: quality, accuracy, clarity, or conciseness.

[0506] In embodiments, the techniques described herein relate to a system, wherein the evaluation data includes both a ranking and a numerical score for each candidate output.

[0507] In embodiments, the techniques described herein relate to a system, wherein the numerical score ranges from one to one hundred.

[0508] In embodiments, the techniques described herein relate to a system, wherein the evaluation subsystem is further configured to request confidence levels from each artificial intelligence model regarding their evaluations.

[0509] In embodiments, the techniques described herein relate to a system, wherein the selection subsystem is configured to weight evaluation data based on the confidence levels when calculating aggregate evaluation scores.

[0510] In embodiments, the techniques described herein relate to a system, wherein the selection subsystem is configured to consider latency information when calculating aggregate evaluation scores.

[0511] In embodiments, the techniques described herein relate to a system, wherein lower latency outputs receive higher aggregate evaluation scores when other evaluation factors are equal.Atty Dkt: 11016-8049PCT

[0512] In embodiments, the techniques described herein relate to a system, further including a learning subsystem configured to store winning output information along with characteristics of the user input to improve future model selection.

[0513] In embodiments, the techniques described herein relate to a system, wherein the evaluation subsystem is configured to send evaluation requests to the multiple artificial intelligence models in parallel.

[0514] In embodiments, the techniques described herein relate to a method for consensus based output evaluation associated with artificial intelligence model unification, the method including: providing, by a computing platform, access to a plurality of artificial intelligence models; generating, by a response generation subsystem, multiple candidate outputs by providing a user input to multiple artificial intelligence models from the plurality of artificial intelligence models; sending, by an evaluation subsystem, each candidate output to each of the multiple artificial intelligence models along with instructions to evaluate all candidate outputs; receiving, by the evaluation subsystem, evaluation data from each of the multiple artificial intelligence models, wherein the evaluation data includes rankings or scores for each candidate output; calculating, by a selection subsystem, aggregate evaluation scores for each candidate output based on the evaluation data from all of the multiple artificial intelligence models; and selecting, by the selection subsystem, a final output from the candidate outputs based on the aggregate evaluation scores.

[0515] In embodiments, the techniques described herein relate to a method, wherein the instructions to evaluate specify evaluation criteria including at least two of: quality, accuracy, clarity, or conciseness.

[0516] In embodiments, the techniques described herein relate to a method, wherein the evaluation data includes both a ranking and a numerical score for each candidate output.

[0517] In embodiments, the techniques described herein relate to a method, wherein the numerical score ranges from one to one hundred.

[0518] In embodiments, the techniques described herein relate to a method, further including requesting confidence levels from each artificial intelligence model regarding their evaluations.

[0519] In embodiments, the techniques described herein relate to a method, wherein calculating aggregate evaluation scores includes weighting evaluation data based on the confidence levels.

[0520] In embodiments, the techniques described herein relate to a method, wherein calculating aggregate evaluation scores includes considering latency information.

[0521] In embodiments, the techniques described herein relate to a method, wherein lower latency outputs receive higher aggregate evaluation scores when other evaluation factors are equal.

[0522] In embodiments, the techniques described herein relate to a method, further including storing winning output information along with characteristics of the user input to improve future model selection.

[0523] In embodiments, the techniques described herein relate to a method, wherein sending evaluation requests includes sending evaluation requests to the multiple artificial intelligence models in parallel. Prompt Model Consensus Ranking (PMCR)

[0524] In embodiments, the techniques described herein relate to a system for consensus based input decomposition evaluation associated with artificial intelligence model unification, the system including: a computing platform configured to provide access to a plurality of artificial intelligence models; a decomposition generation subsystem configured to generate multiple candidate decompositions of a user input by providing the user input to multiple artificial intelligence models from the plurality of artificial intelligence models, wherein each candidate decomposition includes a plurality of component inputs; an evaluation subsystem configured to: send each candidateAtty Dkt: 11016-8049PCT decomposition to each of the multiple artificial intelligence models along with instructions to evaluate all candidate decompositions; receive evaluation data from each of the multiple artificial intelligence models, wherein the evaluation data includes rankings or scores for each candidate decomposition; and a selection subsystem configured to: calculate aggregate evaluation scores for each candidate decomposition based on the evaluation data from all of the multiple artificial intelligence models; and select a final decomposition from the candidate decompositions based on the aggregate evaluation scores for use in processing the user input.

[0525] In embodiments, the techniques described herein relate to a system, wherein the instructions to evaluate specify criteria for assessing quality of decompositions including at least one of: comprehensiveness, proper sequencing, or absence of redundancy.

[0526] In embodiments, the techniques described herein relate to a system, wherein each candidate decomposition includes both the plurality of component inputs and a proposed processing sequence for the component inputs.

[0527] In embodiments, the techniques described herein relate to a system, further including a validation subsystem configured to generate verification inputs to challenge aspects of the candidate decompositions before evaluation.

[0528] In embodiments, the techniques described herein relate to a system, wherein the verification inputs include questions about whether component inputs are in proper sequence and whether the decomposition is comprehensive.

[0529] In embodiments, the techniques described herein relate to a system, wherein the evaluation subsystem is configured to request confidence levels from each artificial intelligence model regarding their evaluations of the candidate decompositions.

[0530] In embodiments, the techniques described herein relate to a system, further including a processing subsystem configured to process the plurality of component inputs from the selected final decomposition using one or more artificial intelligence models.

[0531] In embodiments, the techniques described herein relate to a system, wherein the processing subsystem is configured to use the proposed processing sequence from the final decomposition to determine an order for processing the plurality of component inputs.

[0532] In embodiments, the techniques described herein relate to a system, further including a learning subsystem configured to store information about the selected final decomposition to improve a model selection algorithm for future input decomposition tasks.

[0533] In embodiments, the techniques described herein relate to a system, wherein the decomposition generation subsystem is configured to generate the multiple candidate decompositions in parallel.

[0534] In embodiments, the techniques described herein relate to a method for consensus based input decomposition evaluation associated with artificial intelligence model unification, the method including: providing, by a computing platform, access to a plurality of artificial intelligence models; generating, by a decomposition generation subsystem, multiple candidate decompositions of a user input by providing the user input to multiple artificial intelligence models from the plurality of artificial intelligence models, wherein each candidate decomposition includes a plurality of component inputs; sending, by an evaluation subsystem, each candidate decomposition to each of the multiple artificial intelligence models along with instructions to evaluate all candidate decompositions; receiving, by the evaluation subsystem, evaluation data from each of the multiple artificial intelligence models, wherein the evaluation data includes rankings or scores for each candidate decomposition; calculating, by a selection subsystem, aggregate evaluation scores for each candidate decomposition based on the evaluation data from all of the multiple artificialAtty Dkt: 11016-8049PCT intelligence models; and selecting, by the selection subsystem, a final decomposition from the candidate decompositions based on the aggregate evaluation scores for use in processing the user input.

[0535] In embodiments, the techniques described herein relate to a method, wherein the instructions to evaluate specify criteria for assessing quality of decompositions including at least one of: comprehensiveness, proper sequencing, or absence of redundancy.

[0536] In embodiments, the techniques described herein relate to a method, wherein each candidate decomposition includes both the plurality of component inputs and a proposed processing sequence for the component inputs.

[0537] In embodiments, the techniques described herein relate to a method, further including generating verification inputs to challenge aspects of the candidate decompositions before evaluation.

[0538] In embodiments, the techniques described herein relate to a method, wherein the verification inputs include questions about whether component inputs are in proper sequence and whether the decomposition is comprehensive.

[0539] In embodiments, the techniques described herein relate to a method, further including requesting confidence levels from each artificial intelligence model regarding their evaluations of the candidate decompositions.

[0540] In embodiments, the techniques described herein relate to a method, further including processing the plurality of component inputs from the selected final decomposition using one or more artificial intelligence models.

[0541] In embodiments, the techniques described herein relate to a method, further including using the proposed processing sequence from the final decomposition to determine an order for processing the plurality of component inputs.

[0542] In embodiments, the techniques described herein relate to a method, further including storing information about the selected final decomposition to improve a model selection algorithm for future input decomposition tasks.

[0543] In embodiments, the techniques described herein relate to a method, wherein generating the multiple candidate decompositions includes generating the multiple candidate decompositions in parallel. Confidence Level and Latency

[0544] In embodiments, the techniques described herein relate to a system for quality-weighted model evaluation associated with artificial intelligence model unification, the system including: a computing platform configured to provide access to a plurality of artificial intelligence models; a response generation subsystem configured to generate multiple candidate outputs by providing a user input to multiple artificial intelligence models from the plurality of artificial intelligence models; an evaluation subsystem configured to: send each candidate output to each of the multiple artificial intelligence models along with instructions to evaluate all candidate outputs and provide certainty measures indicating confidence in their evaluations; receive evaluation data from each of the multiple artificial intelligence models, wherein the evaluation data includes scores for each candidate output and certainty measures for each evaluation; a performance measurement subsystem configured to determine temporal performance characteristics for generating each candidate output; and a selection subsystem configured to: calculate weighted evaluation scores for each candidate output based on the evaluation data, the certainty measures, and the temporal performance characteristics; and select a final output from the candidate outputs based on the weighted evaluation scores.

[0545] In embodiments, the techniques described herein relate to a system, wherein the certainty measures include confidence scores on a numerical scale.

[0546] In embodiments, the techniques described herein relate to a system, wherein the selection subsystem is configured to assign greater weight to evaluation data associated with higher certainty measures.Atty Dkt: 11016-8049PCT

[0547] In embodiments, the techniques described herein relate to a system, wherein the temporal performance characteristics include response times for generating each candidate output.

[0548] In embodiments, the techniques described herein relate to a system, wherein the selection subsystem is configured to favor candidate outputs with lower response times when quality scores are similar.

[0549] In embodiments, the techniques described herein relate to a system, wherein the temporal performance characteristics include latency measurements.

[0550] In embodiments, the techniques described herein relate to a system, further including a storage subsystem configured to store the certainty measures along with the evaluation data for use in improving future model selection.

[0551] In embodiments, the techniques described herein relate to a system, wherein the selection subsystem is configured to apply configurable weighting factors to balance consideration of quality scores, certainty measures, and temporal performance characteristics.

[0552] In embodiments, the techniques described herein relate to a system, wherein the configurable weighting factors are adjustable based on user preferences.

[0553] In embodiments, the techniques described herein relate to a system, wherein the evaluation subsystem is configured to request that each artificial intelligence model provide reasoning for their certainty measures.

[0554] In embodiments, the techniques described herein relate to a method for quality-weighted model evaluation associated with artificial intelligence model unification, the method including: providing, by a computing platform, access to a plurality of artificial intelligence models; generating, by a response generation subsystem, multiple candidate outputs by providing a user input to multiple artificial intelligence models from the plurality of artificial intelligence models; sending, by an evaluation subsystem, each candidate output to each of the multiple artificial intelligence models along with instructions to evaluate all candidate outputs and provide certainty measures indicating confidence in their evaluations; receiving, by the evaluation subsystem, evaluation data from each of the multiple artificial intelligence models, wherein the evaluation data includes scores for each candidate output and certainty measures for each evaluation; determining, by a performance measurement subsystem, temporal performance characteristics for generating each candidate output; calculating, by a selection subsystem, weighted evaluation scores for each candidate output based on the evaluation data, the certainty measures, and the temporal performance characteristics; and selecting, by the selection subsystem, a final output from the candidate outputs based on the weighted evaluation scores.

[0555] In embodiments, the techniques described herein relate to a method, wherein the certainty measures include confidence scores on a numerical scale.

[0556] In embodiments, the techniques described herein relate to a method, wherein calculating weighted evaluation scores includes assigning greater weight to evaluation data associated with higher certainty measures.

[0557] In embodiments, the techniques described herein relate to a method, wherein the temporal performance characteristics include response times for generating each candidate output.

[0558] In embodiments, the techniques described herein relate to a method, wherein calculating weighted evaluation scores includes favoring candidate outputs with lower response times when quality scores are similar.

[0559] In embodiments, the techniques described herein relate to a method, wherein the temporal performance characteristics include latency measurements.

[0560] In embodiments, the techniques described herein relate to a method, further including storing the certainty measures along with the evaluation data for use in improving future model selection.Atty Dkt: 11016-8049PCT

[0561] In embodiments, the techniques described herein relate to a method, wherein calculating weighted evaluation scores includes applying configurable weighting factors to balance consideration of quality scores, certainty measures, and temporal performance characteristics.

[0562] In embodiments, the techniques described herein relate to a method, wherein the configurable weighting factors are adjustable based on user preferences.

[0563] In embodiments, the techniques described herein relate to a method, further including requesting that each artificial intelligence model provide reasoning for their certainty measures.

[0564] In embodiments, the techniques described herein relate to a system for managing access control and policy enforcement associated with artificial intelligence model unification, the system including: a computing platform configmed to provide access to a plurality of artificial intelligence models; a policy management subsystem configmed to define and store organizational controls for use of the plurality of artificial intelligence models; an access control subsystem configured to: assign access rights to users based on organizational positions; and enforce the access rights by permitting or denying user requests to access artificial intelligence models based on the organizational positions; an authorization subsystem configured to determine resource allocation for users based on user classifications and the organizational controls; and an audit subsystem configured to capture and store information related to user activities and system events.

[0565] In embodiments, the techniques described herein relate to a system, wherein the organizational controls include rules specifying which users can access which artificial intelligence models.

[0566] In embodiments, the techniques described herein relate to a system, wherein the access control subsystem implements role-based access control wherein access rights are assigned based on user roles within an organization.

[0567] In embodiments, the techniques described herein relate to a system, wherein the authorization subsystem is configmed to assign priority levels to users that determine resource allocation when system capacity is limited.

[0568] In embodiments, the techniques described herein relate to a system, wherein higher priority users receive preferential access to artificial intelligence models and faster response times.

[0569] In embodiments, the techniques described herein relate to a system, further including an authentication subsystem configured to verify user identity using multiple verification factors.

[0570] In embodiments, the techniques described herein relate to a system, wherein the multiple verification factors include at least two of: passwords, biometric data, security tokens, or one-time codes.

[0571] In embodiments, the techniques described herein relate to a system, further including an encryption subsystem configured to apply cryptographic transformation to user inputs and model outputs.

[0572] In embodiments, the techniques described herein relate to a system, wherein cryptographic keys used for the cryptographic transformation are maintained exclusively by clients and not accessible to the computing platform.

[0573] In embodiments, the techniques described herein relate to a system, wherein the audit subsystem is configmed to create logs that include timestamps, user identifiers, actions performed, and artificial intelligence models accessed.

[0574] In embodiments, the techniques described herein relate to a method for managing access control and policy enforcement associated with artificial intelligence model unification, the method including: providing, by a computing platform, access to a plurality of artificial intelligence models; defining and storing, by a policy management subsystem, organizational controls for use of the plurality of artificial intelligence models; assigning, by an access control subsystem, access rights to users based on organizational positions; enforcing, by the access control subsystem, the access rights by permitting or denying user requests to access artificial intelligence models based onAtty Dkt: 11016-8049PCT the organizational positions; determining, by an authorization subsystem, resource allocation for users based on user classifications and the organizational controls; and capturing and storing, by an audit subsystem, information related to user activities and system events.

[0575] In embodiments, the techniques described herein relate to a method, wherein the organizational controls include rules specifying which users can access which artificial intelligence models.

[0576] In embodiments, the techniques described herein relate to a method, wherein assigning access rights includes implementing role-based access control wherein access rights are assigned based on user roles within an organization.

[0577] In embodiments, the techniques described herein relate to a method, wherein determining resource allocation includes assigning priority levels to users that determine resource allocation when system capacity is limited.

[0578] In embodiments, the techniques described herein relate to a method, wherein higher priority users receive preferential access to artificial intelligence models and faster response times.

[0579] In embodiments, the techniques described herein relate to a method, further including verifying user identity using multiple verification factors.

[0580] In embodiments, the techniques described herein relate to a method, wherein the multiple verification factors include at least two of: passwords, biometric data, security tokens, or one-time codes.

[0581] In embodiments, the techniques described herein relate to a method, further including applying cryptographic transformation to user inputs and model outputs.

[0582] In embodiments, the techniques described herein relate to a method, wherein cryptographic keys used for the cryptographic transformation are maintained exclusively by clients and not accessible to the computing platform.

[0583] In embodiments, the techniques described herein relate to a method, wherein capturing and storing information includes creating logs that include timestamps, user identifiers, actions performed, and artificial intelligence models accessed. Automated compliance management

[0584] In embodiments, the techniques described herein relate to a system for automated compliance management associated with artificial intelligence model unification, the system including: a computing platform configured to provide access to a plurality of artificial intelligence models; a compliance subsystem configured to: store regulatory requirements applicable to processing of user data; monitor operations of the plurality of artificial intelligence models to detect potential regulatory violations; and automatically enforce compliance controls to ensure conformance with the regulatory requirements; a user verification subsystem configured to: collect features relating to users; calculate trust scores for users based on the features; and adjust access permissions based on the trust scores; and a transaction security subsystem configured to apply protective measures to transactions involving the plurality of artificial intelligence models.

[0585] In embodiments, the techniques described herein relate to a system, wherein the regulatory requirements include data privacy regulations including at least one of GDPR, CCPA, or HIPAA requirements.

[0586] In embodiments, the techniques described herein relate to a system, wherein the compliance subsystem is configured to automatically block operations that would violate the regulatory requirements.

[0587] In embodiments, the techniques described herein relate to a system, wherein the features relating to users include at least one of: historical usage patterns, authentication history, geographic location, or device information.

[0588] In embodiments, the techniques described herein relate to a system, wherein users with lower trust scores are subject to more restrictive access controls than users with higher trust scores.Atty Dkt: 11016-8049PCT

[0589] In embodiments, the techniques described herein relate to a system, wherein the protective measures include encryption, access logging, and anomaly detection.

[0590] In embodiments, the techniques described herein relate to a system, further including a state monitoring subsystem configured to track status information for entities involved in transactions.

[0591] In embodiments, the techniques described herein relate to a system, wherein the state monitoring subsystem is configured to detect suspicious state changes that may indicate security threats.

[0592] In embodiments, the techniques described herein relate to a system, wherein the compliance subsystem is configured to generate compliance reports documenting adherence to the regulatory requirements.

[0593] In embodiments, the techniques described herein relate to a system, wherein the compliance subsystem is configmed to apply different regulatory requirements based on geographic location of users or location where data is processed.

[0594] In embodiments, the techniques described herein relate to a method for automated compliance management associated with artificial intelligence model unification, the method including: providing, by a computing platform, access to a plurality of artificial intelligence models; storing, by a compliance subsystem, regulatory requirements applicable to processing of user data; monitoring, by the compliance subsystem, operations of the plurality of artificial intelligence models to detect potential regulatory violations; automatically enforcing, by the compliance subsystem, compliance controls to ensure conformance with the regulatory requirements; collecting, by a user verification subsystem, features relating to users; calculating, by the user verification subsystem, trust scores for users based on the features; adjusting, by the user verification subsystem, access permissions based on the trust scores; and applying, by a transaction security subsystem, protective measures to transactions involving the plurality of artificial intelligence models.

[0595] In embodiments, the techniques described herein relate to a method, wherein the regulatory requirements include data privacy regulations including at least one of GDPR, CCPA, or HIPAA requirements.

[0596] In embodiments, the techniques described herein relate to a method, wherein automatically enforcing compliance controls includes automatically blocking operations that would violate the regulatory requirements.

[0597] In embodiments, the techniques described herein relate to a method, wherein the features relating to users include at least one of: historical usage patterns, authentication history, geographic location, or device information.

[0598] In embodiments, the techniques described herein relate to a method, wherein users with lower trust scores are subject to more restrictive access controls than users with higher trust scores.

[0599] In embodiments, the techniques described herein relate to a method, wherein the protective measures include encryption, access logging, and anomaly detection.

[0600] In embodiments, the techniques described herein relate to a method, further including tracking status information for entities involved in transactions.

[0601] In embodiments, the techniques described herein relate to a method, further including detecting suspicious state changes that may indicate security threats.

[0602] In embodiments, the techniques described herein relate to a method, further including generating compliance reports documenting adherence to the regulatory requirements.

[0603] In embodiments, the techniques described herein relate to a method, wherein storing regulatory requirements includes applying different regulatory requirements based on geographic location of users or location where data is processed.Atty Dkt: 11016-8049PCT

[0604] In embodiments, the techniques described herein relate to a system for artificial intelligence model unification and access, the system including: a knowledge representation system configured to store structured information; an information retrieval system configured to retrieve data from the knowledge representation system based on user inputs; a data storage system configured to store embedded data; a processing system configured to: generate a knowledge representation dynamically based on available data without requiring manual schema setup; perform hybrid semantic and lexical processing on the user inputs; combine data from private sources and public sources for grounding processing of the user inputs; and generate outputs based on the retrieved data and the user inputs.

[0605] In embodiments, the techniques described herein relate to a system, wherein the processing system is further configmed to create the knowledge representation dynamically using at least one computational model.

[0606] In embodiments, the techniques described herein relate to a system, wherein the at least one computational model creates the knowledge representation using proprietary data provided by a user.

[0607] In embodiments, the techniques described herein relate to a system, wherein the processing system is further configmed to generate the knowledge representation on-the-fly in response to receiving the user inputs.

[0608] In embodiments, the techniques described herein relate to a system, wherein the hybrid semantic and lexical processing includes: performing semantic vectorization on a fixed amount of information; and determining distances between topics in the user inputs and stored data.

[0609] In embodiments, the techniques described herein relate to a system, wherein the processing system is further configmed to stitch together multiple data storage systems to appear as a unified data storage system.

[0610] In embodiments, the techniques described herein relate to a system, wherein stitching together the multiple data storage systems includes creating a knowledge representation of multiple knowledge representations.

[0611] In embodiments, the techniques described herein relate to a system, wherein the processing system is further configmed to: create custom agents to decompose the user inputs into subtopics; and parallelize processing of the subtopics using the information retrieval system.

[0612] In embodiments, the techniques described herein relate to a system, wherein the processing system is further configmed to select an optimal set of information to include in a context with the user inputs.

[0613] In embodiments, the techniques described herein relate to a system, further including a context management system configured to: maintain a transient cache of information between a context window of a computational model and the data storage system; and automatically reload pertinent information into the context window when the context window is exceeded.

[0614] In embodiments, the techniques described herein relate to a method for grounding artificial intelligence processing, the method including: receiving, by one or more data processors, a user input; dynamically generating, by the one or more data processors, a knowledge representation based on available data without requiring preprocessing or manual schema setup; performing, by the one or more data processors, hybrid processing including semantic processing and lexical processing on the user input; retrieving, by the one or more data processors, information from a data storage system based on the knowledge representation and the user input; combining, by the one or more data processors, data from private sources and public sources for grounding processing of the user input; and generating, by the one or more data processors, an output based on the retrieved information and the user input.

[0615] In embodiments, the techniques described herein relate to a method, wherein dynamically generating the knowledge representation includes using at least one computational model to create relationships across different topics and keywords.Atty Dkt: 11016-8049PCT

[0616] In embodiments, the techniques described herein relate to a method, wherein performing hybrid processing includes: performing semantic vectorization to create embeddings of data; and performing lexical analysis to identify keywords and relationships.

[0617] In embodiments, the techniques described herein relate to a method, further including: decomposing the user input into multiple subtopics using custom agents; and processing the multiple subtopics in parallel.

[0618] In embodiments, the techniques described herein relate to a method, further including stitching together multiple pre-processed data storage systems based on context of the user input.

[0619] In embodiments, the techniques described herein relate to a method, further including selecting which knowledge representations to use based on context of the user input.

[0620] In embodiments, the techniques described herein relate to a method, wherein combining data from private sources and public sources includes automatically managing and selecting private datasets without requiring explicit user configuration.

[0621] In embodiments, the techniques described herein relate to a method, further including: maintaining a transient cache of information between a context window of a computational model and static data; and evaluating pertinent information to reload into the context window when the context window is exceeded.

[0622] In embodiments, the techniques described herein relate to a method, further including performing custom embedding and training for scenarios where standard processing falls short.

[0623] In embodiments, the techniques described herein relate to a method, wherein dynamically generating the knowledge representation includes: prompting a computational model to create a new knowledge representation with seed information; and grounding available information based on the user input.

[0624] In embodiments, the techniques described herein relate to a system for multi-modal and multilingual artificial intelligence processing, the system including: a language processing system configured to handle user inputs and generate outputs in multiple languages; a multi-format processing system configured to process user inputs including multiple data types including text, images, video, and audio; a content generation system configmed to generate outputs in multiple formats; and a processing system configured to: identify data types present in the user inputs; decompose the user inputs into components based on the identified data types; route each component to a computational model suited for processing that component; and generate outputs based on processing results from the computational models.

[0625] In embodiments, the techniques described herein relate to a system, wherein the multi-format processing system is configured to process user inputs including at least two of: text, still images, video, audio, and sensor data.

[0626] In embodiments, the techniques described herein relate to a system, wherein the processing system is further configured to select which of multiple computational models to use based on temporal context, geographic location, or content context associated with the user inputs.

[0627] In embodiments, the techniques described herein relate to a system, wherein the processing system is further configured to route different components of a single user input to different computational models based on capabilities of the computational models.

[0628] In embodiments, the techniques described herein relate to a system, wherein the content generation system is configured to generate outputs including at least two of: text, images, charts, audio, and video.

[0629] In embodiments, the techniques described herein relate to a system, further including an interface system configured to display outputs from multiple computational models in a unified interface.Atty Dkt: 11016-8049PCT

[0630] In embodiments, the techniques described herein relate to a system, wherein the unified interface includes multiple windows, each displaying results from computational models with different capabilities.

[0631] In embodiments, the techniques described herein relate to a system, wherein the processing system is further configmed to identify which of multiple computational models covering same capabilities are superior based on at least one of: temporal nature, context, and geographic location.

[0632] In embodiments, the techniques described herein relate to a system, wherein the language processing system is configured to process user inputs in a first language and generate outputs in a second language different from the first language.

[0633] In embodiments, the techniques described herein relate to a system, wherein the processing system is further configmed to adapt content of the user inputs based on identified data types before routing to the computational models.

[0634] In embodiments, the techniques described herein relate to a method for multi-modal artificial intelligence processing, the method including: receiving, by one or more data processors, a user input including multiple data types; identifying, by the one or more data processors, the multiple data types present in the user input; decomposing, by the one or more data processors, the user input into multiple components based on the identified data types; routing, by the one or more data processors, each component to a computational model suited for processing that component; processing, by the one or more data processors, each component using the routed computational model; and generating, by the one or more data processors, an output based on processing results from the computational models.

[0635] In embodiments, the techniques described herein relate to a method, wherein identifying the multiple data types includes identifying at least two of: text, images, video, audio, and sensor data.

[0636] In embodiments, the techniques described herein relate to a method, wherein routing each component includes selecting computational models based on temporal context, geographic location, or content context associated with the user input.

[0637] In embodiments, the techniques described herein relate to a method, further including generating the output in multiple formats including at least two of: text, images, charts, audio, and video.

[0638] In embodiments, the techniques described herein relate to a method, further including displaying the output in a unified interface that seamlessly integrates results from multiple computational models.

[0639] In embodiments, the techniques described herein relate to a method, wherein the user input includes content in a first language, and wherein generating the output includes generating content in a second language different from the first language.

[0640] In embodiments, the techniques described herein relate to a method, further including selecting which of multiple computational models handling same data types to use based on performance metrics.

[0641] In embodiments, the techniques described herein relate to a method, wherein decomposing the user input includes identifying which computational models are better at processing different aspects of the user input.

[0642] In embodiments, the techniques described herein relate to a method, further including combining data from multiple knowledge representation systems based on the identified data types and context of the user input.

[0643] In embodiments, the techniques described herein relate to a method, further including adapting content of the user input for each component based on capabilities of the computational model to which that component is routed.

[0644] In embodiments, the techniques described herein relate to a system for monitoring and managing user interactions with artificial intelligence models, the system including: a tracking system configured to track userAtty Dkt: 11016-8049PCT behavior and engagement metrics for users interacting with computational models; a monitoring system configured to monitor outcomes associated with the user interactions; a user analysis system configured to generate user representations based on tracked behavior; a targeting system configured to select users or user groups for engagement; and a processing system configmed to : generate engagement metrics based on the tracked user behavior; create user representations that model individual users; autonomously initiate engagement with users based on the engagement metrics; and adapt computational model selection based on the monitored outcomes.

[0645] In embodiments, the techniques described herein relate to a system, wherein the user analysis system is configured to generate digital representations of users that simulate user characteristics and preferences.

[0646] In embodiments, the techniques described herein relate to a system, further including a gamification system configured to apply game mechanics to user interfaces to enhance user engagement.

[0647] In embodiments, the techniques described herein relate to a system, wherein the gamification system is configured to provide rewards, achievements, or incentives based on user interactions.

[0648] In embodiments, the techniques described herein relate to a system, wherein the processing system is further configured to track user states and flows through interaction sequences.

[0649] In embodiments, the techniques described herein relate to a system, further including a dialog system configured to: identify potentially incorrect assumptions in user inputs; generate clarifying questions for users; and incorporate user responses into context for computational model processing.

[0650] In embodiments, the techniques described herein relate to a system, wherein the dialog system is further configured to: state inferred user intent; and request user confirmation of the inferred user intent.

[0651] In embodiments, the techniques described herein relate to a system, wherein the dialog system is further configured to identify ambiguities in user inputs that can be interpreted in multiple ways.

[0652] In embodiments, the techniques described herein relate to a system, wherein the monitoring system is configured to track outcomes including at least one of: accuracy metrics, user satisfaction metrics, task completion metrics, and performance metrics.

[0653] In embodiments, the techniques described herein relate to a system, wherein the targeting system is configured to select users based on user representations and engagement metrics.

[0654] In embodiments, the techniques described herein relate to a method for managing user interactions with artificial intelligence systems, the method including: tracking, by one or more data processors, user behavior for users interacting with computational models; determining, by the one or more data processors, engagement metrics based on the tracked user behavior; generating, by the one or more data processors, user representations that model individual users based on the tracked user behavior; monitoring, by the one or more data processors, outcomes associated with user interactions with the computational models; autonomously initiating, by the one or more data processors, engagement with users based on the engagement metrics; and adapting, by the one or more data processors, computational model selection based on the monitored outcomes.

[0655] In embodiments, the techniques described herein relate to a method, wherein generating user representations includes creating digital twins that represent users and simulate user characteristics.

[0656] In embodiments, the techniques described herein relate to a method, further including applying gamification elements to user interfaces to enhance user engagement.Atty Dkt: 11016-8049PCT

[0657] In embodiments, the techniques described herein relate to a method, further including: identifying potentially incorrect assumptions in user inputs; generating clarifying questions for users; receiving user responses to the clarifying questions; and incorporating the user responses into context for computational model processing.

[0658] In embodiments, the techniques described herein relate to a method, wherein identifying potentially incorrect assumptions includes analyzing ambiguities in user inputs that can be interpreted in multiple ways.

[0659] In embodiments, the techniques described herein relate to a method, further including: inferring user intent from user inputs; presenting the inferred user intent to users; and requesting user confirmation of the inferred user intent.

[0660] In embodiments, the techniques described herein relate to a method, wherein monitoring outcomes includes tracking at least one of: accuracy metrics, user satisfaction metrics, task completion metrics, and performance metrics.

[0661] In embodiments, the techniques described herein relate to a method, further including selecting users or user groups for targeted engagement based on the user representations and engagement metrics.

[0662] In embodiments, the techniques described herein relate to a method, further including tracking user states and flows through interaction sequences to identify patterns.

[0663] In embodiments, the techniques described herein relate to a method, wherein autonomously initiating engagement includes automatically communicating with users without explicit user requests.

[0664] In embodiments, the techniques described herein relate to a system for artificial intelligence model unification and access, the system including: a set of computing devices configured to execute artificial intelligence processing; a processing hardware component including at least one of: a central processing unit, a graphics processing unit, a neural network processor, and an artificial intelligence system-on-chip; a data storage device configmed to store instructions and data; a network infrastructure configured to facilitate communication between the computing devices; and a processing system configured to: receive user inputs from the computing devices; route the user inputs to computational models based on model selection algorithms; process the user inputs using the computational models executed on the processing hardware component; and transmit outputs to the computing devices via the network infrastructure.

[0665] In embodiments, the techniques described herein relate to a system, wherein the processing hardware component includes an artificial intelligence system-on-chip that integrates multiple processing functions on a single integrated circuit.

[0666] In embodiments, the techniques described herein relate to a system, wherein the artificial intelligence system- on-chip includes specialized circuitry for neural network processing.

[0667] In embodiments, the techniques described herein relate to a system, wherein the set of computing devices includes at least one of: mobile devices, tablets, personal computers, laptops, and servers.

[0668] In embodiments, the techniques described herein relate to a system, wherein the network infrastructure includes a cloud computing system.

[0669] In embodiments, the techniques described herein relate to a system, wherein the cloud computing system includes at least one of: private cloud infrastructure, community cloud infrastructure, and hybrid cloud infrastructure.

[0670] In embodiments, the techniques described herein relate to a system, further including a sensor system configmed to: capture data from an environment using sensors; and provide the captured data as input to the computational models.Atty Dkt: 11016-8049PCT

[0671] In embodiments, the techniques described herein relate to a system, wherein the sensors include at least one of: image sensors, video sensors, temperature sensors, pressure sensors, and motion sensors.

[0672] In embodiments, the techniques described herein relate to a system, wherein the processing hardware component includes a converged artificial intelligence chipset that integrates multiple artificial intelligence processing functions.

[0673] In embodiments, the techniques described herein relate to a system, further including an edge computing system configured to perform artificial intelligence processing at an edge location closer to data sources.

[0674] In embodiments, the techniques described herein relate to a method for distributed artificial intelligence processing, the method including: receiving, by computing devices, user inputs; transmitting, by the computing devices, the user inputs via a network infrastructure to a processing system; selecting, by one or more data processors, computational models to process the user inputs based on model selection algorithms; executing, by processing hardware including at least one of a central processing unit, a graphics processing unit, a neural network processor, and an artificial intelligence system-on-chip, the selected computational models to process the user inputs; generating, by the one or more data processors, outputs based on processing results from the computational models; and transmitting, by the one or more data processors, the outputs to the computing devices via the network infrastructure.

[0675] In embodiments, the techniques described herein relate to a method, wherein executing the selected computational models includes using an artificial intelligence system-on-chip that integrates multiple processing functions on a single integrated circuit.

[0676] In embodiments, the techniques described herein relate to a method, wherein the network infrastructure includes a cloud computing system, and wherein executing the selected computational models includes processing the user inputs on cloud-based resources.

[0677] In embodiments, the techniques described herein relate to a method, further including: capturing data from an environment using sensors integrated with the computing devices; and providing the captured data as input to the computational models.

[0678] In embodiments, the techniques described herein relate to a method, wherein capturing data includes capturing at least one of: images, video, temperature data, pressure data, and motion data.

[0679] In embodiments, the techniques described herein relate to a method, further including performing sensor fusion to combine data from multiple sensors before providing the combined data to the computational models.

[0680] In embodiments, the techniques described herein relate to a method, wherein selecting computational models includes distributing processing between cloud-based resources and edge computing systems.

[0681] In embodiments, the techniques described herein relate to a method, wherein the processing hardware includes a converged artificial intelligence chipset that integrates multiple artificial intelligence processing functions.

[0682] In embodiments, the techniques described herein relate to a method, further including optimizing allocation of processing across multiple processing hardware components based on computational requirements.

[0683] In embodiments, the techniques described herein relate to a method, further including storing the outputs and processing history on the computing devices or in a cloud-based data storage device.

[0684] In embodiments, the techniques described herein relate to a system for facilitating transactions and services in an artificial intelligence platform, the system including: a marketplace system configured to provide access to computational models and digital assets; a transaction system configured to process transactions using at least one of: digital payment means, cashless transaction processing, and cryptocurrency; a security system configmed to secureAtty Dkt: 11016-8049PCT transactions and data; a compliance system configured to ensure compliance with regulatory requirements; a resource management system configmed to provision and optimize computing resources; and a processing system configured to: enable users to access computational models via the marketplace system; process user transactions using the transaction system; apply security measures to protect user data and transactions; monitor compliance with legal and regulatory requirements; and optimize allocation of computing resources based on usage patterns.

[0685] In embodiments, the techniques described herein relate to a system, wherein the marketplace system includes an embedded marketplace for digital twins that represent users, devices, or systems.

[0686] In embodiments, the techniques described herein relate to a system, wherein the marketplace system includes an embedded marketplace for computational models that allows users to access multiple models.

[0687] In embodiments, the techniques described herein relate to a system, wherein the transaction system is configured to process transactions using digital wallets that store value for users.

[0688] In embodiments, the techniques described herein relate to a system, wherein the transaction system is configured to process cryptocurrency transactions using blockchain technology.

[0689] In embodiments, the techniques described herein relate to a system, wherein the security system is configured to apply encryption to user inputs and outputs, with encryption keys maintained exclusively by users.

[0690] In embodiments, the techniques described herein relate to a system, wherein the security system is further configmed to implement at least one of: multi-factor authentication, role-based access control, and audit logging.

[0691] In embodiments, the techniques described herein relate to a system, wherein the compliance system is configmed to ensure compliance with data privacy regulations including at least one of: HIPAA, GDPR, and regional data protection laws.

[0692] In embodiments, the techniques described herein relate to a system, wherein the resource management system is configured to optimize at least one of: computational resources, network resources, and energy resources.

[0693] In embodiments, the techniques described herein relate to a system, further including a reporting system configmed to generate reports on usage, configuration, and billing for users.

[0694] In embodiments, the techniques described herein relate to a system, further including a competitive evaluation system configured to: enable multiple computational models to compete in processing user inputs; and generate metrics on model performance for model developers.

[0695] In embodiments, the techniques described herein relate to a method for managing transactions and services in an artificial intelligence platform, the method including: providing, by one or more data processors, access to computational models via a marketplace system; receiving, by the one or more data processors, a transaction request from a user; processing, by the one or more data processors, the transaction using at least one of: digital payment means, cashless transaction processing, and cryptocurrency; applying, by the one or more data processors, security measures to protect user data and the transaction; monitoring, by the one or more data processors, compliance with legal and regulatory requirements; provisioning, by the one or more data processors, computing resources to process user requests; and optimizing, by the one or more data processors, allocation of the computing resources based on usage patterns.

[0696] In embodiments, the techniques described herein relate to a method, wherein providing access to computational models includes providing access via an embedded marketplace that allows users to select from multiple computational models.Atty Dkt: 11016-8049PCT

[0697] In embodiments, the techniques described herein relate to a method, wherein processing the transaction includes processing payment using a digital wallet associated with the user.

[0698] In embodiments, the techniques described herein relate to a method, wherein processing the transaction includes processing cryptocurrency transactions using blockchain technology.

[0699] In embodiments, the techniques described herein relate to a method, wherein applying security measures includes: encrypting user inputs and outputs using encryption keys; and maintaining the encryption keys exclusively with users.

[0700] In embodiments, the techniques described herein relate to a method, wherein applying security measures further includes implementing at least one of: multi-factor authentication, role-based access control, and audit logging.

[0701] In embodiments, the techniques described herein relate to a method, wherein monitoring compliance includes ensuring compliance with data privacy regulations including at least one of: HIPAA, GDPR, and regional data protection laws.

[0702] In embodiments, the techniques described herein relate to a method, wherein optimizing allocation of computing resources includes optimizing at least one of: computational resources, network resources, and energy resources.

[0703] In embodiments, the techniques described herein relate to a method, further including: enabling multiple computational models to compete in processing user inputs; generating performance metrics for the computational models; and providing the performance metrics to model developers to facilitate model improvement.

[0704] In embodiments, the techniques described herein relate to a routing system for artificial intelligence model unification and access, the system including: one or more processors; and one or more memories storing instructions that, when executed by the one or more processors, cause the system to: receive a user input via an input interface; calculate input characteristics data by extracting metadata from the user input, wherein the input characteristics data includes at least one of: key terms, temporal context, or complexity metric; determine configuration parameters for input processing based at least in part on the input characteristics data; select a first set of processing resources from a plurality of available processing resources based at least in part on the input characteristics data and historical performance data; decompose the user input into a plurality of sub-inputs when decomposition logic indicates decomposition is appropriate; issue the user input or the plurality of sub-inputs to the first set of processing resources; receive a first set of outputs from the first set of processing resources; generate validation data including challenge information configured to test accuracy of the first set of outputs; issue the validation data to the first set of processing resources to generate refined outputs; when consensus logic is enabled, obtain ranking data from the first set of processing resources, wherein each processing resource in the first set ranks the refined outputs based on quality metrics; calculate weighted scores for the refined outputs based on the ranking data; and select a final output from the refined outputs based on the weighted scores.

[0705] In embodiments, the techniques described herein relate to a system, wherein the instructions further cause the system to: determine a resource allocation strategy based on budget constraint parameters and current expenditure data; and adjust the determination of configuration parameters based on the resource allocation strategy.

[0706] In embodiments, the techniques described herein relate to a system, wherein calculating the input characteristics data includes: sending a metadata extraction request to a selected processing resource from the plurality of available processing resources; receiving metadata response data from the selected processing resource; and parsing the metadata response data to extract the key terms, the temporal context, and the complexity metric.Atty Dkt: 11016-8049PCT

[0707] In embodiments, the techniques described herein relate to a system, wherein the complexity metric includes a numerical value ranging from 1 to 5 indicating a sophistication level of the user input.

[0708] In embodiments, the techniques described herein relate to a system, wherein decomposing the user input includes: sending a decomposition request to at least one processing resource in the first set of processing resources, wherein the decomposition request instructs the at least one processing resource to break down the user input into a series of interconnected sub-inputs; and receiving decomposition output data including the plurality of sub-inputs.

[0709] In embodiments, the techniques described herein relate to a system, wherein the instructions further cause the system to : when validation logic for input processing is enabled: send validation prompts to the at least one processing resource to challenge accuracy of the decomposition output data; and update the plurality of sub-inputs based on validation responses received from the at least one processing resource.

[0710] In embodiments, the techniques described herein relate to a system, wherein the instructions further cause the system to: maintain performance tracking data associating input characteristics with processing resource performance metrics; update the performance tracking data based on the ranking data and the weighted scores; and utilize the updated performance tracking data when selecting processing resources for subsequent user inputs.

[0711] In embodiments, the techniques described herein relate to a system, wherein the quality metrics include at least two of: accuracy measure, clarity measure, conciseness measure, confidence level, or latency value.

[0712] In embodiments, the techniques described herein relate to a system, wherein the instructions further cause the system to: when agentic processing logic is enabled: analyze the user input to identify decomposition opportunities; create specialized processing agents configured to handle specific sub-tasks identified in the decomposition opportunities; track frequency of agent usage; and when frequency of a particular agent exceeds a threshold value, store the particular agent in an agent repository for future utilization.

[0713] In embodiments, the techniques described herein relate to a system, wherein creating specialized processing agents includes: generating clarification prompts based on assumptions in the decomposition opportunities; presenting the clarification prompts to a user via an output interface; receiving clarification responses from the user; and refining the specialized processing agents based on the clarification responses.

[0714] In embodiments, the techniques described herein relate to a computer-implemented method for routing in an artificial intelligence model unification and access environment, the method including: receiving, by one or more processors, a user input via an input interface; calculating, by the one or more processors, input characteristics data by extracting metadata from the user input, wherein the input characteristics data includes at least one of: key terms, temporal context, or complexity metric; determining, by the one or more processors, configuration parameters for input processing based at least in part on the input characteristics data; selecting, by the one or more processors, a first set of processing resources from a plurality of available processing resources based at least in part on the input characteristics data and historical performance data; decomposing, by the one or more processors, the user input into a plurality of sub-inputs when decomposition logic indicates decomposition is appropriate; issuing, by the one or more processors, the user input or the plurality of sub-inputs to the first set of processing resources; receiving, by the one or more processors, a first set of outputs from the first set of processing resources; generating, by the one or more processors, validation data including challenge information configured to test accuracy of the first set of outputs; issuing, by the one or more processors, the validation data to the first set of processing resources to generate refined outputs; when consensus logic is enabled, obtaining, by the one or more processors, ranking data from the first set of processing resources, wherein each processing resource in the first set ranks the refined outputs based on qualityAtty Dkt: 11016-8049PCT metrics; calculating, by the one or more processors, weighted scores for the refined outputs based on the ranking data; and selecting, by the one or more processors, a final output from the refined outputs based on the weighted scores.

[0715] In embodiments, the techniques described herein relate to a method, further including: determining a resource allocation strategy based on budget constraint parameters and current expenditure data; and adjusting the determination of configmation parameters based on the resource allocation strategy.

[0716] In embodiments, the techniques described herein relate to a method, wherein calculating the input characteristics data includes: sending a metadata extraction request to a selected processing resource from the plurality of available processing resources; receiving metadata response data from the selected processing resource; and parsing the metadata response data to extract the key terms, the temporal context, and the complexity metric.

[0717] In embodiments, the techniques described herein relate to a method, wherein decomposing the user input includes: sending a decomposition request to at least one processing resource in the first set of processing resources, wherein the decomposition request instructs the at least one processing resource to break down the user input into a series of interconnected sub-inputs; and receiving decomposition output data including the plurality of sub-inputs.

[0718] In embodiments, the techniques described herein relate to a method, further including: when validation logic for input processing is enabled: sending validation prompts to the at least one processing resource to challenge accuracy of the decomposition output data; and updating the plurality of sub-inputs based on validation responses received from the at least one processing resource.

[0719] In embodiments, the techniques described herein relate to a method, further including: maintaining performance tracking data associating input characteristics with processing resource performance metrics; updating the performance tracking data based on the ranking data and the weighted scores; and utilizing the updated performance tracking data when selecting processing resources for subsequent user inputs.

[0720] In embodiments, the techniques described herein relate to a method, further including: when agentic processing logic is enabled: analyzing the user input to identify decomposition opportunities; creating specialized processing agents configmed to handle specific sub-tasks identified in the decomposition opportunities; tracking frequency of agent usage; and when frequency of a particular agent exceeds a threshold value, storing the particular agent in an agent repository for future utilization.

[0721] In embodiments, the techniques described herein relate to a method, wherein creating specialized processing agents includes: generating clarification prompts based on assumptions in the decomposition opportunities; presenting the clarification prompts to a user via an output interface; receiving clarification responses from the user; and refining the specialized processing agents based on the clarification responses.

[0722] In embodiments, the techniques described herein relate to a method, wherein the quality metrics include at least two of: accuracy measure, clarity measure, conciseness measure, confidence level, or latency value.

[0723] In embodiments, the techniques described herein relate to a method, further including: storing the historical performance data in a data store, wherein the historical performance data associates input characteristics with performance scores for each processing resource in the plurality of available processing resources.

[0724] In embodiments, the techniques described herein relate to a configuration system for artificial intelligence model unification and access, the system including: one or more processors; and one or more memories storing instructions that, when executed by the one or more processors, cause the system to: maintain a plurality of configmation parameters, wherein the plurality of configuration parameters includes: budget constraint parameters defining expenditure limits for resource usage; quality tier parameters defining quality levels for selection ofAtty Dkt: 11016-8049PCT processing resources; sophistication parameters defining processing depth for at least one of input processing or output processing; automation control parameters defining whether configuration selection is automated or manual; feature enablement parameters defining which processing features are activated; and verbosity parameters defining detail level for outputs generated by processing resources; receive input characteristics data associated with a user input; when automation control parameters indicate automated configuration: calculate optimized configuration values for the plurality of configuration parameters based at least in part on the input characteristics data; and apply the optimized configuration values to control routing of the user input to processing resources; when automation control parameters indicate manual configuration: retrieve user-specified configuration values for the plurality of configuration parameters; and apply the user-specified configuration values to control routing of the user input to processing resources; monitor current expenditure data against the budget constraint parameters; and when current expenditure data exceeds the budget constraint parameters, generate an alert and prevent further processing unless override authorization is received.

[0725] In embodiments, the techniques described herein relate to a system, wherein the budget constraint parameters include: a daily expenditure limit value that resets at a predetermined time; and an override setting that, when activated, allows a user to reset the daily expenditure limit value.

[0726] In embodiments, the techniques described herein relate to a system, wherein the quality tier parameters include a stratified set of quality levels, each quality level associated with a different cost range and performance characteristic.

[0727] In embodiments, the techniques described herein relate to a system, wherein the stratified set of quality levels includes: a first level corresponding to lowest-cost processing resources; a second level corresponding to economical processing resources providing improved performance relative to the first level; a third level corresponding to moderate-cost processing resources providing balanced performance and cost; and a fourth level corresponding to premium processing resources providing highest performance.

[0728] In embodiments, the techniques described herein relate to a system, wherein the sophistication parameters include: input processing sophistication values defining a number of processing resources to utilize for input processing; and output processing sophistication values defining a number of processing resources to utilize for output processing.

[0729] In embodiments, the techniques described herein relate to a system, wherein the input processing sophistication values range from a low setting utilizing a single processing resource to a high setting utilizing multiple processing resources with consensus ranking.

[0730] In embodiments, the techniques described herein relate to a system, wherein the feature enablement parameters include: decomposition enablement indicating whether to decompose inputs into sub-inputs; challenge enablement indicating whether to challenge outputs with validation data; consensus enablement indicating whether to obtain ranking data from multiple processing resources; agentic enablement indicating whether to utilize agent-based processing; and historical ranking enablement indicating whether to use historical performance data for resource selection.

[0731] In embodiments, the techniques described herein relate to a system, wherein calculating the optimized configuration values includes: determining a complexity metric from the input characteristics data; accessing a configuration matrix that maps combinations of the complexity metric, the quality tier parameters, and theAtty Dkt: 11016-8049PCT sophistication parameters to specific feature enablement states; and extracting the optimized configuration values from the configuration matrix.

[0732] In embodiments, the techniques described herein relate to a system, wherein the verbosity parameters include levels ranging from minimal detail with no explanatory context to maximum detail with comprehensive explanations and references.

[0733] In embodiments, the techniques described herein relate to a system, wherein the instructions further cause the system to: provide a user interface enabling a user to override global configuration settings on a per-request basis; and log override events including user identity, timestamp, and overridden settings.

[0734] In embodiments, the techniques described herein relate to a computer-implemented method for configuration management in an artificial intelligence model unification and access environment, the method including: maintaining, by one or more processors, a plurality of configuration parameters, wherein the plurality of configmation parameters includes: budget constraint parameters defining expenditure limits for resource usage; quality tier parameters defining quality levels for selection of processing resources; sophistication parameters defining processing depth for at least one of input processing or output processing; automation control parameters defining whether configuration selection is automated or manual; feature enablement parameters defining which processing features are activated; and verbosity parameters defining detail level for outputs generated by processing resources; receiving, by the one or more processors, input characteristics data associated with a user input; when automation control parameters indicate automated configmation: calculating, by the one or more processors, optimized configuration values for the plurality of configuration parameters based at least in part on the input characteristics data; and applying, by the one or more processors, the optimized configuration values to control routing of the user input to processing resources; when automation control parameters indicate manual configuration: retrieving, by the one or more processors, user-specified configuration values for the plurality of configuration parameters; and applying, by the one or more processors, the user-specified configmation values to control routing of the user input to processing resources; monitoring, by the one or more processors, current expenditure data against the budget constraint parameters; and when current expenditme data exceeds the budget constraint parameters, generating, by the one or more processors, an alert and preventing further processing unless override authorization is received.

[0735] In embodiments, the techniques described herein relate to a method, wherein the budget constraint parameters include: a daily expenditure limit value that resets at a predetermined time; and an override setting that, when activated, allows a user to reset the daily expenditure limit value.

[0736] In embodiments, the techniques described herein relate to a method, wherein the quality tier parameters include a stratified set of quality levels, each quality level associated with a different cost range and performance characteristic.

[0737] In embodiments, the techniques described herein relate to a method, wherein calculating the optimized configuration values includes: determining a complexity metric from the input characteristics data; accessing a configuration matrix that maps combinations of the complexity metric, the quality tier parameters, and the sophistication parameters to specific feature enablement states; and extracting the optimized configuration values from the configuration matrix.

[0738] In embodiments, the techniques described herein relate to a method, wherein the feature enablement parameters include: decomposition enablement indicating whether to decompose inputs into sub-inputs; challenge enablement indicating whether to challenge outputs with validation data; consensus enablement indicating whether toAtty Dkt: 11016-8049PCT obtain ranking data from multiple processing resources; agentic enablement indicating whether to utilize agent-based processing; and historical ranking enablement indicating whether to use historical performance data for resource selection.

[0739] In embodiments, the techniques described herein relate to a method, further including: providing a user interface enabling a user to override global configuration settings on a per-request basis; and logging override events including user identity, timestamp, and overridden settings.

[0740] In embodiments, the techniques described herein relate to a method, further including: dynamically adjusting the budget constraint parameters based on real-time expenditure tracking and remaining budget availability.

[0741] In embodiments, the techniques described herein relate to a method, wherein the sophistication parameters include: input processing sophistication values defining a number of processing resources to utilize for input processing; and output processing sophistication values defining a number of processing resources to utilize for output processing.

[0742] In embodiments, the techniques described herein relate to a method, wherein the input processing sophistication values range from a low setting utilizing a single processing resource to a high setting utilizing multiple processing resources with consensus ranking.

[0743] In embodiments, the techniques described herein relate to a method, wherein the verbosity parameters include levels ranging from minimal detail with no explanatory context to maximum detail with comprehensive explanations and references. DATA

[0744] In embodiments, the techniques described herein relate to a data management system for artificial intelligence model unification and access, the system including: one or more processors; and one or more memories storing instructions that, when executed by the one or more processors, cause the system to: generate and store input decomposition data including a plurality of sub-inputs derived from decomposition of a user input; generate and store validation data including challenge information configured to test accuracy of outputs from processing resources; generate and store input characteristic data including metadata extracted from the user input, wherein the input characteristic data includes: key term data identifying significant terms in the user input; temporal reference data indicating temporal context of the user input; and complexity data indicating a difficulty level of the user input; maintain resource identification data including: input processing resource identifiers for processing resources selected for input processing; output processing resource identifiers for processing resources selected for output processing; and validation resource identifiers for processing resources selected for validation operations; store processed input data representing a final processed version of the user input after input processing operations; store output data representing responses generated by the output processing resource identifiers; store final result data representing a selected output after ranking and validation operations; maintain cost information data associating each processing resource with cost metrics; maintain context storage data configured to optimize input capacity limitations of processing resources; and maintain historical performance data associating input characteristics with performance rankings for processing resources.

[0745] In embodiments, the techniques described herein relate to a system, wherein the instructions further cause the system to: receive a new user input; extract new input characteristic data from the new user input; query the historical performance data using the new input characteristic data to identify highest-ranked processing resources for the new user input; and select processing resources for the new user input based on the highest-ranked processing resources identified from the historical performance data.Atty Dkt: 11016-8049PCT

[0746] In embodiments, the techniques described herein relate to a system, wherein the complexity data includes a numerical value on a scale from 1 to 5, wherein 1 represents lowest complexity and 5 represents highest complexity.

[0747] In embodiments, the techniques described herein relate to a system, wherein the temporal reference data includes an enumeration indicating whether the user input relates to past events, present events, or future events.

[0748] In embodiments, the techniques described herein relate to a system, wherein the validation data includes: a plurality of challenge questions configured to verify accuracy of outputs generated by processing resources; and validation criteria for evaluating responses to the plurality of challenge questions.

[0749] In embodiments, the techniques described herein relate to a system, wherein the input decomposition data includes: a sequence of interconnected sub-inputs, each sub-input representing a portion of the user input; and dependency information indicating relationships between the interconnected sub-inputs.

[0750] In embodiments, the techniques described herein relate to a system, wherein the dependency information indicates whether sub-inputs should be processed synchronously or asynchronously.

[0751] In embodiments, the techniques described herein relate to a system, wherein the context storage data includes: a transient cache of information larger than a context capacity of individual processing resources; embedding data representing vectorized content from prior interactions; and relevance scores indicating which cached information is most pertinent for a current user input.

[0752] In embodiments, the techniques described herein relate to a system, wherein the instructions further cause the system to: when a processing resource approaches its context capacity limit: identify high-relevance information from the context storage data; reload the high-relevance information into an input sent to the processing resource; and remove low-relevance information from the input to maintain the input within the context capacity limit.

[0753] In embodiments, the techniques described herein relate to a system, wherein the cost information data includes: absolute cost values for each processing resource; and relative cost values comparing processing resources within quality tiers.

[0754] In embodiments, the techniques described herein relate to a computer-implemented method for data management in an artificial intelligence model unification and access environment, the method including: generating and storing, by one or more processors, input decomposition data including a plurality of sub-inputs derived from decomposition of a user input; generating and storing, by the one or more processors, validation data including challenge information configured to test accuracy of outputs from processing resources; generating and storing, by the one or more processors, input characteristic data including metadata extracted from the user input, wherein the input characteristic data includes: key term data identifying significant terms in the user input; temporal reference data indicating temporal context of the user input; and complexity data indicating a difficulty level of the user input; maintaining, by the one or more processors, resource identification data including: input processing resource identifiers for processing resources selected for input processing; output processing resource identifiers for processing resources selected for output processing; and validation resource identifiers for processing resources selected for validation operations; storing, by the one or more processors, processed input data representing a final processed version of the user input after input processing operations; storing, by the one or more processors, output data representing responses generated by the output processing resource identifiers; storing, by the one or more processors, final result data representing a selected output after ranking and validation operations; maintaining, by the one or more processors, cost information data associating each processing resource with cost metrics; maintaining, by the one or more processors, context storage data configured to optimize input capacity limitations of processing resources;Atty Dkt: 11016-8049PCT and maintaining, by the one or more processors, historical performance data associating input characteristics with performance rankings for processing resources.

[0755] In embodiments, the techniques described herein relate to a method, further including: receiving a new user input; extracting new input characteristic data from the new user input; querying the historical performance data using the new input characteristic data to identify highest-ranked processing resources for the new user input; and selecting processing resources for the new user input based on the highest-ranked processing resources identified from the historical performance data.

[0756] In embodiments, the techniques described herein relate to a method, wherein the complexity data includes a numerical value on a scale from 1 to 5, wherein 1 represents lowest complexity and 5 represents highest complexity.

[0757] In embodiments, the techniques described herein relate to a method, wherein the validation data includes: a plurality of challenge questions configured to verify accuracy of outputs generated by processing resources; and validation criteria for evaluating responses to the plurality of challenge questions.

[0758] In embodiments, the techniques described herein relate to a method, wherein the input decomposition data includes: a sequence of interconnected sub-inputs, each sub-input representing a portion of the user input; and dependency information indicating relationships between the interconnected sub-inputs.

[0759] In embodiments, the techniques described herein relate to a method, wherein the dependency information indicates whether sub-inputs should be processed synchronously or asynchronously.

[0760] In embodiments, the techniques described herein relate to a method, wherein the context storage data includes: a transient cache of information larger than a context capacity of individual processing resources; embedding data representing vectorized content from prior interactions; and relevance scores indicating which cached information is most pertinent for a current user input.

[0761] In embodiments, the techniques described herein relate to a method, further including: when a processing resource approaches its context capacity limit: identifying high-relevance information from the context storage data; reloading the high-relevance information into an input sent to the processing resource; and removing low-relevance information from the input to maintain the input within the context capacity limit.

[0762] In embodiments, the techniques described herein relate to a method, further including: updating the historical performance data after each processing operation based on: ranking scores received from processing resources; quality metrics of final outputs; and user feedback data when available.

[0763] In embodiments, the techniques described herein relate to a method, wherein the temporal reference data includes an enumeration indicating whether the user input relates to past events, present events, or future events.

[0764] In embodiments, the techniques described herein relate to an investment advisory system utilizing artificial intelligence model unification and access, the system including: one or more processors; and one or more memories storing instructions that, when executed by the one or more processors, cause the system to: receive client financial data including at least one of: risk profile information, investment goal information, current portfolio information, or financial constraint information; generate an advisory input by combining the client financial data with market data obtained from one or more market data sources; route the advisory input to a plurality of artificial intelligence processing resources selected based on historical performance data for investment advisory tasks; receive a plurality of investment recommendation outputs from the plurality of artificial intelligence processing resources; generate validation data including challenge questions configured to test accuracy and suitability of the plurality of investment recommendation outputs; send the validation data to the plurality of artificial intelligence processing resources toAtty Dkt: 11016-8049PCT generate refined investment recommendation outputs; obtain ranking data from the plurality of artificial intelligence processing resources, wherein each processing resource ranks the refined investment recommendation outputs based on quality metrics; calculate weighted scores for the refined investment recommendation outputs based on the ranking data; select a final investment recommendation from the refined investment recommendation outputs based on the weighted scores; and generate a presentation of the final investment recommendation for delivery to a client interface.

[0765] In embodiments, the techniques described herein relate to a system, wherein the plurality of artificial intelligence processing resources are selected based on: metadata extracted from the advisory input indicating investment domain, time horizon, and complexity level; and historical performance rankings associating processing resources with performance in the investment domain.

[0766] In embodiments, the techniques described herein relate to a system, wherein the market data includes at least one of: real-time stock prices, bond yields, commodity prices, market trend data, or economic indicator data.

[0767] In embodiments, the techniques described herein relate to a system, wherein generating the advisory input further includes: decomposing the client financial data into a plurality of sub-inquiries related to different aspects of investment strategy; and generating separate advisory inputs for each sub-inquiry.

[0768] In embodiments, the techniques described herein relate to a system, wherein the plurality of sub-inquiries include at least two of: asset allocation analysis, risk assessment, tax optimization analysis, or rebalancing strategy.

[0769] In embodiments, the techniques described herein relate to a system, wherein the quality metrics include at least two of: alignment with risk profile, expected return optimization, diversification measure, regulatory compliance measure, or tax efficiency measure.

[0770] In embodiments, the techniques described herein relate to a system, wherein the instructions further cause the system to: monitor ongoing market conditions; when market conditions change beyond a threshold level, automatically generate updated advisory inputs; and obtain updated investment recommendations based on the updated advisory inputs.

[0771] In embodiments, the techniques described herein relate to a system, wherein the instructions further cause the system to: extract knowledge from proprietary financial documents using a knowledge graph retrieval-augmented generation system; ground the plurality of artificial intelligence processing resources with the extracted knowledge; and utilize the grounded processing resources to generate the plurality of investment recommendation outputs.

[0772] In embodiments, the techniques described herein relate to a system, wherein the final investment recommendation includes: recommended portfolio adjustments; expected return projections; risk assessment metrics; and rationale explaining the recommended portfolio adjustments.

[0773] In embodiments, the techniques described herein relate to a system, wherein the instructions further cause the system to: track performance of implemented investment recommendations over time; update the historical performance data based on actual performance relative to projected performance; and adjust selection of artificial intelligence processing resources for future advisory inputs based on the updated historical performance data.

[0774] In embodiments, the techniques described herein relate to a computer-implemented method for investment advisory utilizing artificial intelligence model unification and access, the method including: receiving, by one or more processors, client financial data including at least one of: risk profile information, investment goal information, current portfolio information, or financial constraint information; generating, by the one or more processors, an advisory input by combining the client financial data with market data obtained from one or more market data sources; routing, by the one or more processors, the advisory input to a plurality of artificial intelligence processing resources selectedAtty Dkt: 11016-8049PCT based on historical performance data for investment advisory tasks; receiving, by the one or more processors, a plurality of investment recommendation outputs from the plurality of artificial intelligence processing resources; generating, by the one or more processors, validation data including challenge questions configured to test accuracy and suitability of the plurality of investment recommendation outputs; sending, by the one or more processors, the validation data to the plurality of artificial intelligence processing resources to generate refined investment recommendation outputs; obtaining, by the one or more processors, ranking data from the plurality of artificial intelligence processing resources, wherein each processing resource ranks the refined investment recommendation outputs based on quality metrics; calculating, by the one or more processors, weighted scores for the refined investment recommendation outputs based on the ranking data; selecting, by the one or more processors, a final investment recommendation from the refined investment recommendation outputs based on the weighted scores; and generating, by the one or more processors, a presentation of the final investment recommendation for delivery to a client interface.

[0775] In embodiments, the techniques described herein relate to a method, wherein the plurality of artificial intelligence processing resources are selected based on: metadata extracted from the advisory input indicating investment domain, time horizon, and complexity level; and historical performance rankings associating processing resources with performance in the investment domain.

[0776] In embodiments, the techniques described herein relate to a method, further including: decomposing the client financial data into a plurality of sub-inquiries related to different aspects of investment strategy; and generating separate advisory inputs for each sub-inquiry.

[0777] In embodiments, the techniques described herein relate to a method, wherein the plurality of sub-inquiries include at least two of: asset allocation analysis, risk assessment, tax optimization analysis, or rebalancing strategy.

[0778] In embodiments, the techniques described herein relate to a method, wherein the quality metrics include at least two of: alignment with risk profile, expected return optimization, diversification measure, regulatory compliance measure, or tax efficiency measure.

[0779] In embodiments, the techniques described herein relate to a method, further including: monitoring ongoing market conditions; when market conditions change beyond a threshold level, automatically generating updated advisory inputs; and obtaining updated investment recommendations based on the updated advisory inputs.

[0780] In embodiments, the techniques described herein relate to a method, further including: extracting knowledge from proprietary financial documents using a knowledge graph retrieval-augmented generation system; grounding the plurality of artificial intelligence processing resources with the extracted knowledge; and utilizing the grounded processing resources to generate the plurality of investment recommendation outputs.

[0781] In embodiments, the techniques described herein relate to a method, further including: tracking performance of implemented investment recommendations over time; updating the historical performance data based on actual performance relative to projected performance; and adjusting selection of artificial intelligence processing resources for future advisory inputs based on the updated historical performance data.

[0782] In embodiments, the techniques described herein relate to a method, wherein the market data includes at least one of: real-time stock prices, bond yields, commodity prices, market trend data, or economic indicator data.

[0783] In embodiments, the techniques described herein relate to a method, wherein the final investment recommendation includes: recommended portfolio adjustments; expected return projections; risk assessment metrics; and rationale explaining the recommended portfolio adjustments.Atty Dkt: 11016-8049PCT

[0784] A more complete understanding of the disclosure will be appreciated from the description and accompanying drawings and the claims, which follow.BRIEF DESCRIPTION OF THE FIGURES

[0785] The disclosure and the following detailed description of certain embodiments thereof may be understood by reference to the following figures:

[0786] FIG. 1 is a schematic illustrating an example of an Al model unification and routing platform according to some embodiments of the present disclosure.

[0787] FIG. 2A and FIG. 2B are parts of a table illustrating a model smart router optimizer algorithm according to some embodiments of the present disclosure.

[0788] FIG. 3, FIG. 4, FIG. 5, FIG. 6, and FIG. 7 are flow diagrams illustrating model smart routing processes according to some embodiments of the present disclosure.

[0789] FIGS. 8-32 are simplified schematics illustrating systems associated with model smart routing according to some embodiments of the present disclosure.DETAILED DESCRIPTION

[0790] Embodiments described herein relate to Al model unification and access systems.Al model unification and access system

[0791] An Al model unification and routing platform 100 revolutionizes access to Al Models from various leading technology firms. Using a model smart router, this cutting-edge solution offers seamless integration via APIs, a web interface, Excel plugins, and custom proprietary applications, ensuring versatility and ease of use across different operational environments.

[0792] Al model unification and routing platform 100 includes a prompt pre-processor 102, a model smart router 104, a configuration system 110, and a data and networking system 112. In embodiments, component algorithms of platform 100, such as model smart routing, prompt challenge processing, and other methods discussed below may be called Model Design Patterns.

[0793] In embodiments, the platform 100 looks like the other Large Language Models (LLMs) or Al models at a high level. For example, an interface may include a web page where the platform 100 asks “what’s your prompt?” to a user. Platform 100 may include various settings that may be changed by the user. In embodiments, the user may simply enter a prompt and hit enter to use platform 100.

[0794] In embodiments, platform 100 has Unified Model Access where platform 100 issues a single prompt to multiple Al Models simultaneously, with responses efficiently aggregated and delivered from the most suitable Al Model, either automatically selected by the system or pre-configured by the user. In embodiments, this “platform” vision offers path remembrance, breaking down prompts, choosing LLMs, reduced expenses, and reduction of infrastructure problems (e.g., load balancing across LLMs and other Al models).

[0795] In embodiments, platform 100 has service platform capability. For example, platform 100 may embed business analysts, product managers, and engineers for various service tasks, such as helping to automate processes and to assist with taking the capabilities of raw format LLMs and to make them more adaptable to real life usage and viability.

[0796] In embodiments, platform 100 utilizes collaboration of multiple LLMs or other Al models together and leverages multiple LLMs or Al models in a way that no single LLM or Al model is capable of doing. In other words, platform 100 serves as the missing link in technology. For example, when machine learning first became popular, itAtty Dkt: 11016-8049PCT was capable of doing a lot, but applying it to the wrong data set would not allow it to work. A degree of rational thinking is approximated by LLMs to provide that reasoning or missing link.

[0797] Platform 100 leverages Al models in a way that improves the overall experience and improves the overall experience for the user. For example, platform 100 has the models compete in responsiveness, challenges the models (like a good prompt engineer) to get great results, automates repetitive elements of prompt engineering, and achieves the improvement behind the scenes with only one input prompt. Furthermore, the improvement may be accomplished in an economical way. Platform 100 may explode one prompt into many using history, metadata, and past experiences with models.

[0798] In embodiments, platform 100 has configurations for specific industries or tasks. For example, a platform 100 configured for the financial world may automate back office features. A platform 100 configured for reconciliation and clearing may automate rote processes that hundreds of people are doing and may get to answers very quickly using problem solutions described herein. A platform 100 configmed for a front office may have capabilities in financial trading and research data.

[0799] LLMs are undergoing rapid improvements, and each model has strengths and weaknesses. For example, one model may be good at sounding authoritative, but may not be good at being concise unless prompted, may not be good at getting to the heart, and may hallucinate more with increasing amounts of data.

[0800] In embodiments, a user interface may be similar to an interface of an LLM, but is not tied to any particular company or model. For example, platform 100 may have a simple interface where the user can select down to what they want. A user would not need to send the same prompt to multiple Al models, but still may gain the benefit of comparing the output from each of those multiple Al models.

[0801] In embodiments, platform 100 improves the user experience with Al models. For example, platform 100 enables more self-servicing for a better output with reduced hallucination, improved accuracy, and improvement identification for an organization so the organization can protect intellectual property while still having access to the intellectual property.

[0802] In embodiments, platform 100 may relate to strategy companies or service companies that help companies automate to reduce expenses and improve their portfolios. For example, platform 100 may relate to a specific product that can apply Al, automate to reduce workforce, and can leverage data. Companies may run agents on worker desktops to break down work products in an automated manner to create work product data. Platform 100 may then take the work product data and / or other data and use the techniques described herein to generate a recommendation for CEOs and presidents on where to focus for automation. The recommendation may include, for example, a blueprint of how to accomplish the recommendation.

[0803] In embodiments, platform 100 removes the barriers of using Al and makes self-servicing easier. In embodiments, specially trained LLMs may be used for specific tasks within platform 100. For example, pharmaceutical, medical, and legal industries may have specially trained LLMs or other Al models.

[0804] In embodiments, platform 100 captures metadata related to what LLMs are better at what topics. Platform 100 may automatically route prompts related to the topics to the LLMs that are identified as better for specific topics. In embodiments, platform 100 uses LLMs to identify what LLM models give the best responses among a plurality of LLMs to be evaluated.

[0805] The following table shows various features of platform 100, including alternative names:Atty Dkt: 11016-8049PCT

[0806] The following table shows algorithms associated with platform 100:Atty Dkt: 11016-8049PCTPrompt pre-processor

[0807] Prompt pre-processor 102 includes a user dialog agent 150, a setting optimizer 152, a repetition identifier 154, a prompt metadata extractor 156, a KG-RAG data retriever 160, a prompt decomposer and chainer, a chaining response challenge 126, and a chaining consensus ranking 130.

[0808] Prompt pre-processor 102 analyzes, manipulates, and improves the original input prompt entered by the user. In embodiments, pre-prompt processing includes determining a complexity level of the prompt. For example, prompt pre-processor 102 may analyze for content and context, data plus questions, and picture, graph, chart, and books. Prompt pre-processor 102 may collect, generate, or identify metadata to assist with the pre-processing, such as metadata for key words, complexity rankings (e.g., 1-5), differentiation between data and questions, a temporal nature (e.g., past, present, or future), and the like.

[0809] User dialog agent 150 expands Pre-Prompt processing to an agentic practice, which may include customized Agents. User dialog agent 150 may validate the assumptions of the Model’s agentic approach and solicit further necessary detail about the proposed Agent Decomposition to improve the chances of success and the accuracy.

[0810] Setting optimizer 152 may automatically select various settings for configuration system 110 based on the input prompt, a budget, the complexity of the prompt, enterprise settings, or other factors.

[0811] Repetition identifier 154 compares the input prompt with historical input prompts and prompt analysis to identify areas where all or part of a response may be provided without use of Al models or with reduced use of Al models.

[0812] Prompt decomposer and chainer 155 employs prompt decomposition and chaining to improve the quality of the results, address Model hallucination, and self-identify improvements and changes to the initial problem or prompt. Prompt decomposer and chainer 155 breaks down the user prompt into a series of smaller, interconnected prompts that can be processed sequentially by a Model to achieve a more precise and accurate answer with minimal hallucination and a higher degree of accuracy and conciseness. For example, the smaller, interconnected prompts may be sub-prompts that are stored as at least part of Prompt Decomposition Data (PDD).

[0813] Prompt metadata (PMD) may be created during prompt processing to be utilized throughout the Model Smart Router (MSR) process. Prompt metadata may include, for example, metadata for the following elements: Keywords, Temporal Reference (Past, Present, or Future), and Complexity (1 to 5, where 1 is low and 5 is high).

[0814] Prompt Challenge Data (PCD) may be created during prompt processing to be utilized in the Automated Response Challenging (ARC) process performed by ranking system 106. The prompts challenge data contains a series of challenge questions to determine the accuracy of an answer generated by a Model. The challenge questions directly relate to the prompt and aim to validate the answer, ensuring minimal hallucination.Atty Dkt: 11016-8049PCT

[0815] In embodiments, prompt decomposition and chaining may be performed or omitted based on a Prompt Decomposition and Chaining (PDC) setting. For example, if Prompt Decomposition & Chaining (PDC) is turned off, then prompt decomposer and chainer 155 may use the entered user prompt as the prompt to be sent by model smart router 104 to the model (e.g., Set Final Prompt = User Prompt). If Prompt Decomposition & Chaining is On, then prompt decomposer and chainer 155 may extract prompt models, extract prompt decomposition data, and send the decomposition prompt. For example, prompt decomposer and chainer 155 may extract Prompt Model(s) from Prompt Historical Ranked Models data 140 based on Prompt Model Consensus Ranking (PMCR) setting value (1 or more Models). Prompt decomposer and chainer 155 may, for example, extract Prompt Decomposition Data (PDD) from the Prompt Model(s). Prompt decomposer and chainer 155 may further send the decomposition prompt to each of the Prompt Model(s) and set PDD equal to the result.

[0816] In embodiments, an example of a prompt sent to an LLM requesting decomposition and chaining may be: For the provided prompt, break it down into a series of smaller, interconnected prompts that can be processed sequentially by an Al Model to achieve a more precise and accurate answer with minimal hallucination and higher degree of accuracy and conciseness. Ensure each smaller prompt is clear, specific, and directly related to the original prompt's intent. Only provide the prompts, no explanation or context is needed. Each prompt should be simply provided with prefixes of “Promptl”, “Prompt2”, “Prompt3”, etc. Here is the prompt: 'User Prompt gets placed here’.

[0817] In embodiments, Automated Response Challenging (PARC) may be set in the configuration of platform 100. For example, if PARC (Prompt Automated Response Challenging) is set to “On,” then response challenger 180 provisioned for prompt provisioned ranking system 162 may iterate over the responses from Prompt Model(s) and send challenge questions) to improve PDD(s). Prompt provisioned ranking system 162 may further update PDD(s) to the improved results per Prompt Model(s).

[0818] For example, response challenger 180 may prompt an LLM with the prompt: Are you sure each of your previously provided prompts is in the proper sequence? Are you sure each of the previously provided prompts comprehensively and succinctly represents the original previously provided prompt and breaks it down into smaller concise steps? Finally, are there any prompts that were provided that are repetitive and not needed? Please provide a new list of prompts based on these challenge questions, no explanation or context is needed, only the prompts. Each prompt should be simply provided with prefixes of “Promptl”, “Prompt2”, “Prompt3”, etc.

[0819] Prompt metadata extractor 156 extracts prompt decomposition data. Prompt Decomposition Data (PDD) may be created during prompt processing to be utilized in the Prompt Decomposition and Chaining (PDC) process. In the example provided, the prompt decomposition data is a list of smaller, interconnected prompts related to the original user prompt.

[0820] RAG data retriever 160 retrieves conventional retrieval augmented generation (RAG) data and performs a dynamic knowledge-graph RAG (KG-RAG) function that allows users to provide data in a standardized format for hybrid processing (Semantic + Lexical) including combining private and public data for grounding of model processing.

[0821] In embodiments, RAG data retriever 160 may utilize KG-RAG methods that require pre-processing and schema definitions and pre-setup of the RAG + LLM environment.

[0822] In embodiments, the dynamic KG-RAG capability combines Applied Al and Generative Al techniques to broadly manage and select private data sets so users no longer need to imply private data for grounding.Atty Dkt: 11016-8049PCT

[0823] In embodiments, platform 100 may be configured for modular use case implementation. For example, platform 100 may enable the breakdown of complex use cases into manageable tasks, allowing for consistent and repeatable outcomes.

[0824] In embodiments, prompt processing includes historical ranking, prompt automated response challenging, and prompt model consensus ranking. Each of historical ranking, prompt automated response challenging, and prompt model consensus ranking applies to the response from the model. Prompt automated response challenging and model consensus ranking are relevant to the prompt processing itself. In embodiments, prompt processing includes prompt chaining and prompt decomposition to break down prompts into a series of iterative prompts. For example, a question may be broken down into smaller questions to send to the model.

[0825] In embodiments, platform 100 has features for reducing LLM hallucinations. The features help exclude bad data from the response and hone in on what the user is interested in. In embodiments, the process includes separating the data from the question and breaking the question into three to five questions (sub-prompts). For example, platform 100 may use an LLM for breaking down the questions by instructing the LLM to break down the question and giving relevant context. In embodiments, the prompt automated response challenging (PARC) asks questions to the LLM in response to receiving a response to test the accuracy of the response, as will be described below. In embodiments, the response challenge questions may be pre-selected with questions known to cause LLMs to correct errors in their responses. In embodiments, automated response challenging uses LLMs to suggest response questions that can be used as dynamic response challenges.

[0826] In embodiments, the sub-prompts may be bundled and sent as a single prompt to reduce API calls. In embodiments, the sub-prompts may be individually sent to different LLMs with context from the other sub-prompts or sub-prompt responses. For example, each sub-prompt may have a different focus or objective for which different LLMs may excel.

[0827] Consensus ranking compares responses from different models when the consensus ranking option is enabled. For example, the consensus ranker may send a prompt to several LLMs, gather responses from the LLMs, create a new prompt, and ask for rankings. The new prompt may have, for example, the original prompt, the response, and the responses of the other LLMs. Asking for rankings may include, for example, asking the LLM to respond back to me, ranking the responses, and giving a score from 1-100 based on the quality, conciseness and value of the responses. The ranked responses are used to select the most favorable response including the best decomposed prompt to use in the prompt processing.

[0828] Historical ranking selection may utilize metadata and past scores to choose the top 3 LLMs to be used.

[0829] Prompt provisioned ranking system 162 is a provisioned version of ranking system 106, which will be described below. Specifically, system 162 may be configured for response challenging and consensus ranking during prompt decomposition and chaining.

[0830] Prompt pre-processor 102 may be configured to calculate prompt metadata (PMD). Based on MCT (Model Cost Tiering), model smart router 104 or configuration system 110 chooses a model and sends a metadata extraction prompt, collecting the PMD based on the response. For example, prompt pre-processor 102 may send the following prompt: For the provided prompt, capture and list the metadata for the following elements: Keywords, Temporal Reference (Past, Present, or Future), and Complexity (1 to 5, where 1 is low and 5 is high). Do not answerthe provided prompt. Only provide the metadata, no explanation or context is needed. Format each answer in CSV as follows: Element-Name: values in csv format. Here is the prompt: ‘User Prompt gets placed here’.Atty Dkt: 11016-8049PCT

[0831] In embodiments, Prompt Model Consensus Ranking (PMCR) is a setting that may be set to “on” or “off.” prompt pre-processor 102 may use the PDD data as the final prompt if the PMCR setting is “off” (e.g., Set Final Prompt = PDD(s) (there should be just one)). Prompt pre-processor 102 may request prompt decomposition, score PDDs, and choose a winning PDD if the PMCR setting is “on.” For example, prompt pre-processor 102 may request prompt decomposition by sending a prompt to each Prompt Model containing the original prompt and the PDD(s) from each Prompt Model, asking each Prompt Model to rank and score the quality of each Prompt Model(s) PDD response. Prompt pre-processor 102 may score PDD(s) by using the responses from each Prompt Model to calculate a weighted score for each Prompt Model PDD. Prompt pre-processor 102 may choose a winning PDD by choosing the PDD with the highest score as the winner and setting the final prompt equal to the winning prompt (e.g., Set Final Prompt = PDD winner).

[0832] For example, prompt decomposer and chainer 155 of prompt pre-processor 102 may send the following prompt to an LLM: I will provide you with an original prompt and the responses from multiple Al Models to that prompt. Your task is to evaluate and rank these responses based on quality, accuracy, clarity, and conciseness. Please rank each Model response with a number from 1 to n, where n is the total number of Models provided. Additionally, assign a score from 1 to 100 representing the overall quality, accuracy, clarity, and conciseness of each response, with 1 being the worst and 100 being the best.

[0833] The output format should be: ‘Model Name: Model Rank, Model Score’, with each Model on a new line.

[0834] The original prompt is: ‘Prompt Decomposition Chaining’ prompt gets placed here’

[0835] The LLM responses are: ‘Model 1: Model 1 response gets placed here’

[0836] ‘Model 2: Model 2 response gets placed here’ ‘Model 3: Model 3 response gets placed here’.

[0837] The metadata extraction prompt accommodates large prompts of various types and separates content from questions. In embodiments, parsing prompts include parsing prompts into types, segment content, and further directing / informing subsequent processing. As shown in the example above, the metadata may include the following variables: Keywords (e.g., Type, List), Temporal Reference (e.g., Enum), Complexity (e.g., Type, Integer).

[0838] In embodiments, prompt pre-processor 102 may be configmed to Extract & Calculate Settings and Calculate Prompt Processing Settings Data. For example, setting optimizer 152 may extract and calculate the settings and calculate prompt processing settings data.

[0839] In embodiments, if Prompt Detailed Processing Settings are set with the “Automatic” setting to “On,” setting optimizer 152 may use an MSR Optimizer algorithm (MO A) to determine the settings for prompt processing. For example, the MOA may have prompt MOA inputs, prompt MOA outputs, and a prompt MOA algorithm. The prompt MOA inputs may include, for example, a prompt complexity level, a model cost tiering (MCT) setting level, and a prompt processing sophistication (PPS) setting level. The prompt MOA outputs may include, for example, a ‘Prompt Automated Response Challenge’ (PARC) Setting (On or Off) and a ‘Prompt Model Consensus Ranking’ (PMCR) Setting (On or Off). An example of the prompt MOA algorithm is shown as an algorithm matrix in FIGS. 2 A and 2B.

[0840] In embodiments, if Prompt Detailed Processing Settings are set with the “Automatic” setting to “Off,” setting optimizer 152 may, for example, load ’Prompt Automated Response Challenge’ (PARC) Setting (On or Off) and load ‘Prompt Model Consensus Ranking’ (PMCR) Setting (On or Off). For example, the settings may be pre-set by the user or by an enterprise policy at configuration system 110.

[0841] In embodiments, prompt pre-processor 102 calculates MSR processing settings data. For example, setting optimizer 152 may calculate the MSR processing setting data. In embodiments, if the “MSR Detailed Processing”Atty Dkt: 11016-8049PCTSettings are set with the “Automatic” setting to “On,” setting optimizer 152 may use the MOA to determine the settings for MSR processing using MSR inputs, MSR outputs, and the MOA algorithm. The MSR inputs may be, for example, Prompt Complexity Level, Model Cost Tiering (MCT) setting level, and MSR Processing Sophistication (MPS) setting level. An example of the MOA algorithm is illustrated in matrix form in FIGS. 2A and 2B. The MSR outputs may be, for example, ‘Historical Ranking Selector’ (HRS) Setting (On or Off), ‘Prompt Decomposition Chaining’ (PDC) Setting (On or Off), ‘Automated Response Challenge’ (ARC) Setting (On or Off), and ‘Model Consensus Ranking’ (MCR) Setting (On or Off).

[0842] In embodiments, if the “MSR Detailed Processing” Settings are set with Automatic to “Off,” setting optimizer 152 may load preselected settings. For example, setting optimizer 152 may load a ‘Historical Ranking Selector’ (HRS) Setting (On or Off), a ‘Prompt Decomposition Chaining’ (PDC) Setting (On or Off), an ’Automated Response Challenge’ (ARC) Setting (On or Off), and / or a ‘Model Consensus Ranking’ (MCR) Setting (On or Off).Model smart router

[0843] Model smart router 104 includes a result provisioned ranking system 170. In embodiments, model Smart Router 104 has Adaptive Learning Capabilities (ALC) to intelligently direct prompts to the ideal Model or Models, depending on settings (see Model Consensus Ranking (MCR)), optimizing based on the specific content and context of the request, ensuring precision and relevance in responses. This method optimizes routing decisions and response quality over time based on a Historical Ranking Selector (HRS) and Model Cost Tiering (MCT) to enhance the platform's effectiveness.

[0844] In embodiments, model smart router 104 includes a cache for repetitive requests. In embodiments, model smart router 104 has a local store to optimize the limited context windows of LLMs.

[0845] In embodiments, Model Smart Router (MSR) 104 selects models, iterates over the models, evaluates automated response challenging, and evaluates model consensus ranking. To select models, model smart router 104 may extract Prompt Model(s) from Historical Ranking Selector (HRS) based on HRS setting value and Model Consensus Ranking (MCR) setting value using the user’s original prompt (1 or more Models). For example, Prompt Models(s) may be stored in result historical ranked models data 142.

[0846] To iterate over the Prompt Model(s), model smart router 104 may send the final prompt to the models and collect the response(s) from the model(s). For example, model smart router 104 may send the Final Prompt and set Prompt results = Results from each Prompt Model(s).

[0847] To evaluate automated response challenging, model smart router 104 may extract Challenge Model from Model Smart Router (MSR) based on Model Cost Tiering (MCT), may use the Challenge Model to extract the Prompt Challenge Data (PCD), may iterate over the Prompt results from the Prompt Model(s) and send PCD to improve the Prompt results, and may update Prompt results to the improved results per Prompt Model(s).

[0848] To evaluate model consensus ranking, model smart router 104 may proceed based on a Model Consensus Ranking (MCR) setting. If MCR (Model Consensus Ranking) is Off, model smart router 104 may Set Final Result = Prompt result (there should be just one). If MCR (Model Consensus Ranking) is On, model smart router 104 may send a prompt to each Prompt Model containing the original prompt and the Prompt Results from each Prompt Model. The prompt to each Prompt Model may ask each Prompt Model to rank and score the quality of each Prompt Model(s) Prompt Result.

[0849] For example, model smart router 104 may send the prompt: I will provide you with an original prompt and the responses from multiple Models to that prompt. Your task is to evaluate and rank these responses based on quality,Atty Dkt: 11016-8049PCT accuracy, clarity, and conciseness. Please rank each Model response with a number from 1 to n, where n is the total number of Models provided. Additionally, assign a score from 1 to 100 representing the overall quality, accuracy, clarity, and conciseness of each response, with 1 being the worst and 100 being the best.

[0850] The output format should be: ‘Model Name: Model Rank, Model Score’, with each Model on a new line.

[0851] The original prompt is: ‘Prompt Decomposition Chaining’ prompt gets placed here’

[0852] The LLM responses are: ‘Modell: Model 1 response gets placed here’ ‘Model 2: Model 2 response gets placed here’ ‘Model 3: Model 3 response gets placed here’

[0853] Model smart router 104 may then use the responses from each Prompt Model to calculate a weighted score for each Prompt Model Prompt Result and choose the Prompt Result with the highest score as the winner. Model smart router 104 may then Set Final Result = Prompt Result winner. In embodiments, model smart router 104 then provides the final result to the user in response to the user prompt.Ranking system

[0854] Ranking system 106 ranks models and / or model outputs. Ranking system 106 includes response challenger 180 and consensus ranker 182.

[0855] In embodiments, the model evaluations include a confidence level that is stored and used to improve the Historical Model Selection process. In embodiments, ranking system 106 considers latency information in ranking.

[0856] In embodiments, ranking system 106 uses a Historical Ranking Selector (HRS) with a process by which meta data for every user prompt is collected, stored and associated with ranking data for each utilized Model to keep track of which Model was best at answering information about the provided nature of the prompt. Model smart router 104 utilizes this information to aid in the selection of which Models to utilize for future prompts in result provisioned ranking system 170.

[0857] In embodiments, ranking system 106 uses a Prompt Processing Historical Ranking Selector (PPHRS). PPHRS is a specialized version of the Historical Ranking Selector (HRC) for prompt processing in prompt provisioned ranking system 162. PPHRS is highly customized for the purpose of selecting which Models to utilize in the extraction of Prompt metadata (PMD), Prompt Decomposition Data (PDD) and Prompt Challenge Data (PCD).

[0858] In embodiments, consensus ranker 182 performs a process where multiple Models generate responses to a user-provided prompt. These responses are then evaluated by the same Models, which rank each response based on quality, accuracy, clarity, and conciseness. This competitive and evaluative approach ensures that the best responses are identified and highlighted. Consensus ranker 182 may be used both for prompt pre-processing in prompt provisioned ranking system 162 and for result ranking in result provisioned ranking system 170.

[0859] In embodiments, response challenger 180 performs Automated Response Challenging (ARC). Automated Response Challenging is the process of asking Model challenge questions, Prompt Challenge Data (PCD), immediately after it provides results to the user prompt. The challenge questions aim to validate the answer, ensuring minimal hallucination and improving the accuracy of the results.Configuration system

[0860] Configuration system 110 has settings and configurations for inline cost management 120, prompt processing sophistication 122, model smart router processing sophistication 124, chaining response challenge 126, prompt consensus ranking 128, response historical ranking 130, prompt decomposition 132, response challenge 134, and prioritized access control 136.Atty Dkt: 11016-8049PCT

[0861] In embodiments, the settings and configmations may be set by setting optimizer 152 using MSR Optimizer Algorithm (MO A). For example, the MOA may automatically select the MSR settings for HRS, PDC, ARC and MCR based on the complexity level of the prompt (requires Prompt Meta Data), the Model Cost Tiering (MCT) setting, and the MSR Processing Sophistication (MPS) setting.

[0862] In embodiments, settings management is based on the entered user prompt. Considerations for setting management include cost expectations, daily budget, balancing hallucinations versus cost, and the like. The user may select the settings, or the settings may be automatically calculated. In embodiments, configuration system 110 has global settings that may be overridden by the user. For example, the user may ask for the fastest, non-hallucinating response and configuration system 110 may override user settings if the response would be clearly superior.

[0863] Inline cost management 120 may include daily budget limits for cost. For example, the budget may include a daily budget limit and a daily budget override. For the daily budget limit, once the limit has been reached, the user may be prompted and prevented from entering more prompts without changing the limit or overriding it. In embodiments, the daily budget may reset at midnight local time. For the daily budget override, the user may reset the override for the day if permissioned. For daily budget overrides, inline cost management 120 may keep a log of when and who set the budget override, and the budget override may reset at midnight to off.

[0864] In embodiments, the model cost tiering may include cost levels. For example, the model cost tiering may be grouped into levels 1 through 4. Level 1 may be a “cheap tier” representing the lowest cost range Models. These lowest cost range Models are highly affordable and designed to be budget friendly. They offer basic functionality and quality, making them suitable for the most cost-conscious consumers. Level 2 may be an “economical” tier with Models slightly higher in cost than Level 1, the Leve...

Claims

Atty Dkt: 11016-8049PCTCLAIMSWhat is claimed is:Al MODEL UNIFICATION AND ACCESS SYSTEM1. An artificial intelligence model unification and routing platform comprising: a prompt pre-processor configured to receive user input and prepare prompts for processing by one or more artificial intelligence models; a model smart router configured to direct prompts to artificial intelligence models based on prompt characteristics and historical performance data; a ranking system configured to evaluate responses from multiple artificial intelligence models to determine highest quality outputs; a configuration system configured to store and manage settings for inline cost management, prompt processing sophistication, and model smart router processing sophistication; and a data and networking system configmed to manage data storage and network communications for the platform, wherein the data and networking system includes prompt historical ranked models data storing historical performance information for artificial intelligence models.

2. The platform of claim 1, wherein the prompt pre-processor includes a prompt metadata extractor configmed to analyze complexity level of incoming prompts by examining content, context, data elements, questions, and included media.

3. The platform of claim 2, wherein the prompt metadata extractor is configured to generate metadata comprising keywords, complexity rankings on a scale from 1 to 5, differentiation between data and questions, and temporal nature indicators.

4. The platform of claim 1, wherein the prompt pre-processor includes a repetition identifier configured to compare incoming prompts with historical prompt data to identify areas where all or part of a response is provided without invoking artificial intelligence models.

5. The platform of claim 1, wherein the model smart router includes adaptive learning capabilities configured to improve routing decisions over time through tracking and scoring of artificial intelligence models based on consensus ranking processes.

6. The platform of claim 1, wherein the model smart router includes a cache for repetitive requests, a local store to optimize context windows, and implements unified model access allowing a single prompt to be issued to multiple artificial intelligence models simultaneously.

7. The platform of claim 1, wherein the ranking system includes a response challenger configured to perform automated response challenging by generating challenge questions related to a prompt and submitting challenge questions to artificial intelligence models immediately after the models provide initial responses.

8. The platform of claim 7, wherein the challenge questions are configured to validate answers provided by the models to ensure minimal hallucination and improve accuracy of results.

9. The platform of claim 1, wherein the ranking system includes a consensus ranker configured to implement a process where multiple artificial intelligence models generate responses to a user-provided prompt, and those responses are evaluated by same or different models which rank each response based on quality, accuracy, clarity, and conciseness.Atty Dkt: 11016-8049PCT10. The platform of claim 1, wherein the configuration system includes inline cost management configured to enable users to establish daily budget limits for costs associated with artificial intelligence model usage, and once a limit has been reached, the system prompts the user and prevents entry of additional prompts unless the user resets the daily budget or provides an override.

11. A method for providing unified access to multiple artificial intelligence models comprising: receiving, by a prompt pre-processor, user input and preparing prompts for processing by one or more artificial intelligence models; analyzing, by the prompt pre-processor, complexity level of incoming prompts by examining content, context, data elements, questions, and included media; directing, by a model smart router, prompts to appropriate artificial intelligence models based on prompt characteristics, historical performance data, cost considerations, and configuration settings; generating responses from multiple artificial intelligence models; evaluating, by a ranking system, responses from the multiple artificial intelligence models to determine highest quality outputs; storing, by a data and networking system, historical performance information for artificial intelligence models in prompt historical ranked models data; and improving routing decisions over time through tracking and scoring of artificial intelligence models based on consensus ranking processes.

12. The method of claim 11, further comprising generating, by the prompt pre-processor, metadata comprising keywords, complexity rankings, differentiation between data and questions, and temporal nature indicators.

13. The method of claim 11, further comprising comparing, by the prompt pre-processor, incoming prompts with historical prompt data to identify areas where all or part of a response is provided without invoking artificial intelligence models.

14. The method of claim 11, further comprising issuing a single prompt to multiple artificial intelligence models simultaneously and aggregating responses.

15. The method of claim 11, further comprising performing automated response challenging by generating challenge questions related to a prompt and submitting challenge questions to artificial intelligence models immediately after the models provide initial responses to validate answers and ensure minimal hallucination.

16. The method of claim 11, further comprising implementing a consensus ranking process where multiple artificial intelligence models generate responses to a user-provided prompt, and those responses are evaluated by same or different models which rank each response based on quality, accuracy, clarity, and conciseness.

17. The method of claim 11, further comprising establishing, through inline cost management, daily budget limits for costs associated with artificial intelligence model usage, and once a limit has been reached, prompting the user and preventing entry of additional prompts unless the user resets the daily budget or provides an override.

18. The method of claim 11, further comprising accessing model cost data to determine relative or absolute costs of utilizing different artificial intelligence models, allowing the system to balance quality requirements against budget constraints.

19. The method of claim 11, further comprising accessing context window data containing information from previous or intermediate responses from same or different models, enabling the system to maintain coherent multiturn conversations.Atty Dkt: 11016-8049PCT20. The method of claim 11, further comprising automatically selecting settings for historical ranking selector, prompt decomposition and chaining, automated response challenging, and model consensus ranking based on complexity level of a prompt as determined from prompt metadata, model cost tiering setting, and model smart router processing sophistication setting.UNIFIED MULTI-MODEL USER INTERFACE TOPOLOGIES21. A unified multi-model user interface system for interacting with an artificial intelligence model unification platform comprising: a user interface configured to serve as a point of interaction between users and the artificial intelligence model unification platform, wherein the user interface presents a text input field where users enter prompts and displays responses generated by artificial intelligence models; a dashboard configured to provide a visual interface presenting information and metrics associated with artificial intelligence model usage, including usage statistics and performance metrics; a web interface configured to provide a browser-based graphical user interface through which users interact with the platform; browser integration configmed to extend functionality of the platform into web browsers through browser extensions; embedded multi-model copilots configured to provide artificial intelligence assistance integrated directly into applications and workflows where users are working; and office productivity software plugins configured to integrate the platform with office productivity applications, wherein the office productivity software plugins include spreadsheet plugins, word processing plugins, and presentation plugins.

22. The system of claim 21, wherein the user interface is configured to enable users to select specific artificial intelligence models from a plurality of available models and displays model selection options organized by capability, cost tier, or performance characteristics.

23. The system of claim 21, wherein the dashboard is configured to display usage statistics showing number of prompts submitted, number of responses generated, distribution of prompts across different artificial intelligence models, and total costs incurred.

24. The system of claim 21, wherein the dashboard is configured to present performance metrics including response times, hallucination rates, accuracy scores, and user satisfaction ratings.

25. The system of claim 21, wherein the dashboard is configured to display sustainability metrics showing carbon footprint of artificial intelligence operations and environmental impact of model usage.

26. The system of claim 21, wherein the web interface is implemented using hypertext markup language, cascading style sheets, and JavaScript, enabling access from any device with a modem web browser without requiring installation of specialized software.

27. The system of claim 21, wherein the browser integration is configured to enable users to invoke artificial intelligence models directly from web pages they are viewing, allowing users to summarize web page content, translate text, or answer questions about displayed information.

28. The system of claim 21, wherein the embedded multi-model copilots are configured to appear as inline assistants within text editors, code editors, or email clients, offering suggestions, completing text, answering questions, and performing tasks based on user's current context.Atty Dkt: 11016-8049PCT29. The system of claim 21, wherein the spreadsheet plugins are configured to integrate with spreadsheet applications to provide artificial intelligence data analysis, formula generation, chart creation, and data visualization capabilities.

30. The system of claim 29, wherein the spreadsheet plugins are configured to enable users to generate formulas by describing desired calculations in natural language, automatically detect and correct errors in formulas, and generate explanatory comments for complex calculations.

31. A method for providing unified multi-model user interfaces comprising: presenting, by a user interface, a text input field where users enter prompts and displaying responses generated by artificial intelligence models; providing, by a dashboard, a visual interface presenting usage statistics showing number of prompts submitted, number of responses generated, distribution of prompts across different artificial intelligence models, and total costs incurred; providing, by a web interface, a browser-based graphical user interface implemented using hypertext markup language, cascading style sheets, and JavaScript; extending, by browser integration, functionality of an artificial intelligence platform into web browsers to enable users to invoke artificial intelligence models directly from web pages; providing, by embedded multi-model copilots, artificial intelligence assistance integrated directly into applications as inline assistants; and integrating, by office productivity software plugins, the artificial intelligence platform with office productivity applications including spreadsheet applications, word processing applications, and presentation applications.

32. The method of claim 31, further comprising enabling users to select specific artificial intelligence models from a plurality of available models and displaying model selection options organized by capability, cost tier, or performance characteristics.

33. The method of claim 31, further comprising presenting, by the dashboard, performance metrics including response times, hallucination rates, accuracy scores, user satisfaction ratings, and sustainability metrics showing carbon footprint of artificial intelligence operations.

34. The method of claim 31, further comprising enabling, by the browser integration, users to summarize web page content, translate text, answer questions about displayed information, or perform other artificial intelligence assisted tasks without navigating away from the web page.

35. The method of claim 31, further comprising capturing, by the browser integration, selected text from web pages and automatically including that text as context when submitting prompts to artificial intelligence models.

36. The method of claim 31, further comprising monitoring, by the embedded multi-model copilots, user actions and automatically invoking appropriate artificial intelligence models to provide contextually relevant assistance without requiring explicit prompts.

37. The method of claim 31, further comprising providing, by spreadsheet plugins, artificial intelligence powered data analysis, formula generation, chart creation, and data visualization capabilities by enabling users to generate formulas by describing desired calculations in natural language.

38. The method of claim 31, further comprising providing, by word processing plugins, artificial intelligence powered writing assistance, document summarization, tone adjustment, and content generation capabilities.Atty Dkt: 11016-8049PCT39. The method of claim 31, further comprising providing, by presentation plugins, artificial intelligence powered slide generation, content organization, design suggestions, and speaker notes creation.

40. The method of claim 31, further comprising implementing, by a user experience system, consistent interaction patterns, visual design elements, and terminology across the web interface, browser integration, embedded copilots, and office productivity software plugins.CUSTOM INTEGRATION AND FRAMEWORKS41. A custom integration and frameworks system for integrating an artificial intelligence model unification platform with external applications comprising: a customizable integration framework configmed to provide a flexible architecture enabling users to configure integrations between the platform and external systems without requiring extensive custom software development; a modular use case implementation configured to provide a structured approach to deploying the platform for specific business scenarios by breaking complex use cases into manageable, reusable components; an extract-transform-load system configured to extract data from source systems, transform the data into formats suitable for artificial intelligence model processing, and load the data into storage systems accessible to the platform; software development kits providing libraries, code samples, documentation, and tools enabling software developers to build applications and integrations with the platform; and an application programming interface providing programmatic interfaces through which external applications interact with the platform.

42. The system of claim 41, wherein the customizable integration framework implements a visual workflow designer enabling business users to create integration workflows by connecting pre-built integration components in a graphical interface.

43. The system of claim 41, wherein the customizable integration framework provides libraries of pre-built connectors for enterprise software systems, cloud services, and data sources, each connector implementing authentication mechanisms, data access methods, and data transformation operations specific to that system.

44. The system of claim 41, wherein the customizable integration framework enables users to define data mapping rules specifying how fields in source systems correspond to fields in destination systems and how data values should be transformed.

45. The system of claim 41, wherein the modular use case implementation defines use case templates capturing common patterns of artificial intelligence model usage, required data sources, expected outputs, and success criteria.

46. The system of claim 45, wherein the modular use case implementation enables users to select use case templates, configure parameters specific to their business context, and deploy functional implementations without starting from scratch.

47. The system of claim 41, wherein the extract-transform-load system connects to databases, data warehouses, file systems, application programming interfaces, and other data sources to extract data on scheduled intervals or in response to triggering events.

48. The system of claim 41, wherein the extract-transform-load system performs transformation operations including data cleansing removing duplicates and correcting errors, data normalization converting data to consistent formats and units, data enrichment adding derived fields or supplemental information, data aggregationAtty Dkt: 11016-8049PCT summarizing detailed data at higher levels of granularity, and data filtering removing irrelevant or sensitive information.

49. The system of claim 41, wherein the software development kits are available for multiple programming languages including Python, Java, JavaScript, C#, Ruby, and Go, enabling developers to work in their preferred languages.

50. The system of claim 49, wherein the software development kit for Python includes a client library encapsulating application programming interface calls to the platform, providing Python functions for submitting prompts, retrieving responses, managing settings, and accessing historical data.

51. A method for integrating an artificial intelligence model unification platform with external applications comprising: providing, by a customizable integration framework, a flexible architecture enabling users to configure integrations between the platform and external systems through a visual workflow designer; providing, by a modular use case implementation, use case templates capturing common patterns of artificial intelligence model usage, required data sources, expected outputs, and success criteria; extracting, by an extract-transform-load system, data from databases, data warehouses, file systems, and application programming interfaces; transforming, by the extract-transform-load system, the extracted data through data cleansing, data normalization, data enrichment, data aggregation, and data filtering; loading, by the extract-transform-load system, transformed data into storage systems accessible to the platform; providing, by software development kits, libraries and code samples in multiple programming languages enabling developers to build applications and integrations; and providing, by an application programming interface, programmatic interfaces through which external applications submit prompts, retrieve responses, query historical prompts and responses, and manage configuration settings.

52. The method of claim 51, further comprising enabling users to create integration workflows by connecting prebuilt integration components in a graphical interface without requiring extensive custom software development.

53. The method of claim 51, further comprising providing libraries of pre-built connectors for popular enterprise software systems, cloud services, and data sources, each connector implementing authentication mechanisms, data access methods, and data transformation operations specific to that system.

54. The method of claim 51, further comprising enabling users to define data mapping mles specifying how fields in source systems correspond to fields in destination systems and how data values should be transformed including converting date formats, normalizing text, or applying business rules.

55. The method of claim 51, further comprising enabling users to select use case templates, configure parameters specific to their business context, and deploy functional implementations rapidly.

56. The method of claim 51, further comprising implementing, by the extract-transform-load system, incremental loading strategies identifying and processing only data that has changed since last extraction, reducing processing time and resource consumption.Atty Dkt: 11016-8049PCT57. The method of claim 51, further comprising maintaining, by the extract-transform-load system, metadata about data lineage tracking which source systems provided data, what transformations were applied, when data was loaded, and which downstream systems consumed the data.

58. The method of claim 51, further comprising providing, by the software development kits, command-line tools enabling developers to interact with the platform from terminal environments, submit test prompts, inspect responses, manage application programming interface keys, and monitor usage metrics.

59. The method of claim 51, further comprising implementing, by the application programming interface, a RESTful web service using HTTP methods and returning responses in JSON format following standard REST conventions for resource naming and status code usage.

60. The method of claim 51, further comprising implementing, by the application programming interface, rate limiting to prevent individual clients from consuming excessive resources.DATA AND INTELLIGENCE DYNAMICS61. A data and intelligence dynamics system for managing data movement, transformation, storage, and processing operations supporting artificial intelligence model operations comprising: a staging, switching, and routing system configured to manage movement of data through a platform from sources to processing systems to destinations, wherein the staging system temporarily stores data in intermediate storage locations; a sensor and data fusion system configured to combine data from multiple sensors or data sources to produce more accurate, complete, or reliable information than could be obtained from any single source; edge systems configured to perform computation and data processing at network edge, close to data sources and end users; databases and feeds configmed to provide persistent storage and real-time data streams that the platform accesses during operation; an outcome tracking system configmed to monitor results achieved through use of the platform by measuring key performance indicators; machine learning, artificial intelligence, and trained neural networks configured to provide predictive and analytical capabilities; and intelligent agents configured as autonomous software entities trained to perform specific tasks by monitoring environments for triggering conditions, making decisions about actions to take, executing actions, and learning from outcomes.

62. The system of claim 61, wherein the staging system implements storage tiers with different performance and cost characteristics, automatically moving data between fast storage and slower storage based on access patterns and age of data.

63. The system of claim 61, wherein the switching system routes data from one point to another within the platform, directing data flows based on routing rules, data characteristics, and destination availability, and implements load balancing distributing data processing across multiple servers or processing nodes.

64. The system of claim 61, wherein the routing system determines optimal paths for data to travel from sources to destinations, considering factors including network bandwidth, latency, cost, and data privacy requirements.Atty Dkt: 11016-8049PCT65. The system of claim 61, wherein the sensor and data fusion system receives data from heterogeneous sensors measuring different physical phenomena and implements Kalman filtering combining noisy measurements from multiple sensors with predictions from physical models to estimate system states with reduced uncertainty.

66. The system of claim 61, wherein the edge systems include edge servers deployed in cellular base stations, retail stores, factories, or vehicles that process data locally to reduce latency, bandwidth consumption, and dependence on network connectivity to cloud services.

67. The system of claim 61, wherein the edge systems execute lightweight artificial intelligence models that perform inference operations locally, sending only results or summarized data to cloud systems.

68. The system of claim 61, wherein the databases include relational databases implementing SQL for structured data storage, NoSQL databases for flexible schema and horizontal scalability, and time-series databases optimized for storing and querying temporal data.

69. The system of claim 61, wherein the outcome tracking system captures outcome data including task completion rates, accuracy of artificial intelligence generated responses compared to ground truth, user satisfaction scores, business metrics, and operational metrics.

70. The system of claim 69, wherein the outcome tracking system implements A / B testing frameworks that randomly assign incoming prompts to different processing strategies, compare outcomes across strategies, and identify statistically significant performance differences.

71. A method for managing data and intelligence dynamics comprising: managing, by a staging system, movement of data through a platform by temporarily storing data in intermediate storage locations before the data is moved to final destinations; routing, by a switching system, data from one point to another within the platform by directing data flows based on routing rules, data characteristics, and destination availability; combining, by a sensor and data fusion system, data from multiple sensors or data sources to produce more accurate information than could be obtained from any single source; processing, by edge systems, data locally at network edge to reduce latency and bandwidth consumption; providing, by databases and feeds, persistent storage and real-time data streams; monitoring, by an outcome tracking system, results achieved through use of the platform by measuring key performance indicators including task completion rates and accuracy of artificial intelligence generated responses; providing, by machine learning and artificial intelligence systems, predictive and analytical capabilities; and performing, by intelligent agents, specific tasks by monitoring environments for triggering conditions, making decisions about actions, executing actions, and learning from outcomes.

72. The method of claim 71, further comprising implementing, by the staging system, storage tiers with different performance and cost characteristics, automatically moving data between fast storage and slower storage based on access patterns and age of data.

73. The method of claim 71, further comprising implementing, by the switching system, load balancing that distributes data processing across multiple servers or processing nodes, monitors health and performance of processing nodes, and automatically redirects traffic away from failed or overloaded nodes.Atty Dkt: 11016-8049PCT74. The method of claim 71, further comprising implementing, by the routing system, policy-based routing applying business rules to determine data paths, such as requiring that personally identifiable information remains within specific geographic regions.

75. The method of claim 71, further comprising receiving, by the sensor and data fusion system, data from heterogeneous sensors measuring different physical phenomena, such as combining visual data from cameras with distance measurements from lidar sensors and motion data from accelerometers.

76. The method of claim 71, further comprising executing, by the edge systems, lightweight artificial intelligence models that perform inference operations locally, sending only results or summarized data to cloud systems, thereby reducing data transmission costs.

77. The method of claim 71, further comprising implementing, by the edge systems, model caching that stores frequently used artificial intelligence models locally and implementing federated learning that trains artificial intelligence models using data distributed across edge devices without transmitting raw data to centralized servers.

78. The method of claim 71, further comprising associating, by the outcome tracking system, outcomes with specific artificial intelligence models, prompt processing strategies, and configurations used, enabling analysis of which approaches yield best results.

79. The method of claim 71, further comprising analyzing, by intelligent agents, incoming support tickets, classifying tickets by urgency and topic, routing tickets to appropriate specialists, and suggesting responses based on historical resolution patterns.

80. The method of claim 71, further comprising implementing, by a software-defined optimization system, programmatic control over system resources and configurations to achieve performance objectives including dynamic routing based on network conditions and application priorities.AGENTIC PROCESSING81. An agentic processing system for implementing agent-based approaches to prompt processing comprising: a prompt agentic processing system configured to utilize and create agents based on frequency and commonality of agent usage patterns, wherein the prompt agentic processing system analyzes incoming prompts to identify sub-tasks that could be handled by specialized agents; a proprietary knowledge agent catalog containing previously created agents organized by function and domain; a user dialog system configured to validate assumptions of agentic approach and solicit further necessary detail, wherein after an artificial intelligence model decomposes a prompt and generates agents to handle specific tasks, the user dialog system initiates dialog to confirm that agent decomposition assumptions align with user expectations; and an agent decomposition and refinement system configured to break down agents into finer-grained components and improve agent performance.

82. The system of claim 81, wherein the prompt agentic processing system searches the proprietary knowledge agent catalog for existing agents matching identified sub-tasks and invokes matching agents to process corresponding portions of prompts.

83. The system of claim 81, wherein the prompt agentic processing system creates new agents by submitting metaprompts to artificial intelligence models requesting agent definitions for specific sub-tasks when incoming prompts require capabilities not covered by existing agents.Atty Dkt: 11016-8049PCT84. The system of claim 81, wherein the prompt agentic processing system tracks agent usage frequency, identifies agents invoked repeatedly, and promotes frequently used agents to the proprietary knowledge agent catalog for reuse in future prompt processing.

85. The system of claim 81, wherein the prompt agentic processing system monitors commonality of agent types across different prompts and use cases, identifies agent capabilities applicable to multiple domains, and generalizes agents to broaden their applicability.

86. The system of claim 81, wherein the prompt agentic processing system breaks down complex prompts into hierarchies of sub-prompts, creates agent definitions specifying inputs each agent requires, processing each agent performs, and outputs each agent produces, and establishes dependencies between agents.

87. The system of claim 81, wherein the user dialog system presents agent decomposition plans to users, explaining which sub-tasks have been identified and how each will be addressed, enabling users to verify that decomposition captures their intent.

88. The system of claim 81, wherein the user dialog system explicitly states assumptions when agent decomposition makes assumptions about ambiguous aspects of prompts and requests user confirmation or correction.

89. The system of claim 81, wherein the user dialog system prompts users for additional information about proposed agent decomposition, seeking any overlooked aspects, nuances, or details that could improve agent accuracy and precision.

90. The system of claim 81, wherein the agent decomposition and refinement system analyzes complex agents to identify opportunities for further subdivision, creating sub-agents that handle narrower scopes of functionality and enable more specialized processing.

91. A method for agentic processing comprising: analyzing, by a prompt agentic processing system, incoming prompts to identify sub-tasks that could be handled by specialized agents; searching, by the prompt agentic processing system, a proprietary knowledge agent catalog for existing agents matching identified sub-tasks; invoking, by the prompt agentic processing system, matching agents to process corresponding portions of prompts; creating, by the prompt agentic processing system, new agents by submitting meta-prompts to artificial intelligence models requesting agent definitions for specific sub-tasks when incoming prompts require capabilities not covered by existing agents; tracking, by the prompt agentic processing system, agent usage frequency and promoting frequently used agents to the proprietary knowledge agent catalog; initiating, by a user dialog system, dialog to confirm that agent decomposition assumptions align with user expectations; and refining, by an agent decomposition and refinement system, existing agent definitions based on performance observations.

92. The method of claim 91, further comprising monitoring, by the prompt agentic processing system, commonality of agent types across different prompts and use cases, identifying agent capabilities applicable to multiple domains, and generalizing agents to broaden applicability.Atty Dkt: 11016-8049PCT93. The method of claim 91, further comprising breaking down, by the prompt agentic processing system, complex prompts into hierarchies of sub-prompts, creating agent definitions specifying inputs each agent requires, processing each agent performs, and outputs each agent produces.

94. The method of claim 91, further comprising establishing, by the prompt agentic processing system, dependencies between agents indicating which agent outputs serve as inputs to other agents and coordinating agent execution by invoking independent agents in parallel.

95. The method of claim 91, further comprising presenting, by the user dialog system, agent decomposition plans to users, explaining which sub-tasks have been identified and how each will be addressed, enabling users to verify that decomposition captures their intent.

96. The method of claim 91, further comprising explicitly stating, by the user dialog system, assumptions when agent decomposition makes assumptions about ambiguous aspects of prompts and requesting user confirmation or correction.

97. The method of claim 91, further comprising prompting, by the user dialog system, users for additional information about proposed agent decomposition, seeking any overlooked aspects, nuances, or details that could improve agent accuracy and precision.

98. The method of claim 91, further comprising analyzing, by the agent decomposition and refinement system, complex agents to identify opportunities for further subdivision and creating sub-agents that handle narrower scopes of functionality.

99. The method of claim 91, further comprising modifying, by the agent decomposition and refinement system, agent logic to handle edge cases that caused failures, adjusting agent parameters to improve output quality, and updating agent model selections when newer models demonstrate better performance.

100. The method of claim 91, further comprising implementing, by the agent decomposition and refinement system, feedback loops where agent outputs are evaluated against ground truth or user corrections, agents that consistently produce errors are flagged for refinement, and refinement operations modify agent definitions to address identified deficiencies.MODELS AS AGENTS101. A models as agents system for treating artificial intelligence models as autonomous agents comprising: an agent interface wrapper configured to wrap artificial intelligence models with agent interfaces standardizing how models are invoked, how inputs are provided, how outputs are retrieved, and how errors are handled; a role definition system configured to configure models with role definitions specifying types of tasks each model handles, input formats each model accepts, output formats each model generates, and quality characteristics users can expect from each model; a model agent communication protocol configured to enable models to request assistance from other models when encountering inputs beyond their capabilities, delegate sub-tasks to more specialized models, and negotiate with other models to resolve conflicts when different models generate inconsistent outputs; and a composite agent system configured to configure groups of models as composite agents that present unified interfaces while internally distributing work across multiple models.

102. The system of claim 101, wherein the agent interface wrapper enables models to be composed into processing pipelines where outputs from one model serve as inputs to another.Atty Dkt: 11016-8049PCT103. The system of claim 101, wherein the role definition system indicates that a particular model specializes in text summarization, accepting long-form documents as input and generating concise summaries as output, while another model specializes in sentiment analysis, accepting text samples as input and generating sentiment classifications with confidence scores as output.

104. The system of claim 101, wherein the role definition system assigns models to agent teams where multiple models collaborate to accomplish complex tasks, with each model contributing its specialized capabilities.

105. The system of claim 101, wherein a model agent encountering a prompt requesting both text generation and image creation recognizes that it lacks image generation capabilities, invokes an image generation model agent to handle an image portion, and combines outputs from both agents to produce a complete response.

106. The system of claim 101, wherein the model agent communication protocol is configured to track model agent performance, measuring task completion rates, output quality scores, and resource consumption, using performance data to inform model selection decisions.

107. The system of claim 101, wherein the composite agent system includes a composite agent for multilingual translation including multiple translation models specialized for different language pairs, automatically routing translation requests to appropriate specialized models based on source and target languages.

108. The system of claim 101, wherein the composite agent system includes a composite agent for financial analysis including models for numerical calculation, market trend prediction, risk assessment, and narrative generation, orchestrating these models to produce comprehensive financial analyses.

109. The system of claim 101, wherein the composite agent system configures composite agents with fallback logic that attempts alternative models when primary models fail or produce low-quality outputs.

110. The system of claim 101, wherein the agent interface wrapper standardizes error handling across models having different native error reporting mechanisms.

111. A method for treating artificial intelligence models as autonomous agents comprising: wrapping, by an agent interface wrapper, artificial intelligence models with agent interfaces standardizing how models are invoked, how inputs are provided, how outputs are retrieved, and how errors are handled; configuring, by a role definition system, models with role definitions specifying types of tasks each model handles, input formats each model accepts, output formats each model generates, and quality characteristics users can expect; enabling, by a model agent communication protocol, models to request assistance from other models when encountering inputs beyond their capabilities and to delegate sub-tasks to more specialized models; configuring, by a composite agent system, groups of models as composite agents that present unified interfaces while internally distributing work across multiple models; and tracking, by a performance monitoring system, model agent performance including task completion rates, output quality scores, and resource consumption.

112. The method of claim 111, further comprising enabling, by the agent interface wrapper, models to be composed into processing pipelines where outputs from one model serve as inputs to another.

113. The method of claim 111, further comprising indicating, by the role definition system, that a particular model specializes in a specific task type and assigning models to agent teams where multiple models collaborate to accomplish complex tasks.Atty Dkt: 11016-8049PCT114. The method of claim 111, further comprising invoking, by a model agent encountering a prompt requesting multiple types of content, specialized model agents to handle portions requiring capabilities the model agent lacks and combining outputs from multiple agents to produce complete responses.

115. The method of claim 111, further comprising negotiating, by model agents through the communication protocol, with other models to resolve conflicts when different models generate inconsistent outputs.

116. The method of claim 111, further comprising including, by the composite agent system, a composite agent for multilingual translation with multiple translation models specialized for different language pairs and automatically routing translation requests to appropriate specialized models.

117. The method of claim 111, further comprising including, by the composite agent system, a composite agent for financial analysis with models for numerical calculation, market trend prediction, risk assessment, and narrative generation, orchestrating these models to produce comprehensive analyses.

118. The method of claim 111, further comprising configuring, by the composite agent system, composite agents with fallback logic that attempts alternative models when primary models fail or produce low-quality outputs, improving reliability and robustness.

119. The method of claim 111, further comprising using, by a model selection system, performance data including task completion rates, output quality scores, and resource consumption to inform model selection decisions.

120. The method of claim 111, further comprising decoupling, by the agent interface wrapper, prompt processing logic from specific model implementations, enabling models to be replaced or upgraded without modifying prompt processing workflows.MODEL-USER PROMPT INTERACTION121. A model-user prompt interaction system for enabling artificial intelligence models to ask clarifying questions comprising: a prompt interception system configured to intercept prompts before models process them and analyze prompts for ambiguities, uncertainties, or missing information; a clarifying question generator configured to generate clarifying questions that help resolve identified ambiguities, uncertainties, or missing information; a user dialog interface configured to present questions to users and receive user responses; a prompt augmentation system configured to augment original prompts with clarifying information received from users before submitting prompts to models for processing; and a validation system configured to validate model responses by generating challenge questions assessing whether model outputs properly address user intent as clarified through dialog.

122. The system of claim 121, wherein the prompt interception system identifies ambiguous terminology in prompts where words or phrases could be interpreted in multiple ways.

123. The system of claim 121, wherein the clarifying question generator generates questions requesting users to specify intended meanings when ambiguous terminology is identified and incorporates user selections into prompt context.

124. The system of claim 121, wherein the prompt interception system identifies missing information required to fully address prompts, such as prompts requesting analysis of data without specifying which data to analyze or prompts requesting generation of content without specifying desired tone or style.Atty Dkt: 11016-8049PCT125. The system of claim 121, wherein the clarifying question generator generates questions requesting missing information and presents questions to users in priority order with most important questions presented first.

126. The system of claim 121, wherein the prompt interception system detects implicit assumptions in prompts that may not align with user intent and explicitly states detected assumptions.

127. The system of claim 126, wherein the user dialog interface enables users to confirm assumptions or provide corrections and the prompt augmentation system adjusts prompt context based on user feedback.

128. The system of claim 121, wherein the prompt interception system identifies branching points where prompts could proceed down multiple paths depending on user preferences, and the user dialog interface presents alternative paths to users for selection.

129. The system of claim 121, wherein the system implements progressive clarification where initial questions address high-level aspects of prompts and subsequent questions drill down into details.

130. The system of claim 129, wherein when processing a prompt requesting creation of a presentation, the system is configured to first ask about a purpose and an audience of the presentation, then asks about desired length and structure, then asks about visual style preferences and specific content to include.

131. A method for model-user prompt interaction comprising: intercepting, by a prompt interception system, prompts before models process them; analyzing, by the prompt interception system, prompts for ambiguities, uncertainties, or missing information; generating, by a clarifying question generator, clarifying questions that help resolve identified ambiguities, uncertainties, or missing information; presenting, by a user dialog interface, questions to users; receiving, by the user dialog interface, user responses; augmenting, by a prompt augmentation system, original prompts with clarifying information received from users; and validating, by a validation system, model responses by generating challenge questions assessing whether model outputs properly address user intent as clarified through dialog.

132. The method of claim 131, further comprising identifying, by the prompt interception system, ambiguous terminology in prompts where words or phrases could be interpreted in multiple ways and generating questions requesting users to specify intended meanings.

133. The method of claim 131, further comprising identifying, by the prompt interception system, missing information required to fully address prompts and generating questions requesting missing information presented to users in priority order.

134. The method of claim 131, further comprising detecting, by the prompt interception system, implicit assumptions in prompts that may not align with user intent and explicitly stating detected assumptions.

135. The method of claim 134, further comprising enabling, by the user dialog interface, users to confirm assumptions or provide corrections and adjusting prompt context based on user feedback.

136. The method of claim 131, further comprising identifying, by the prompt interception system, branching points where prompts could proceed down multiple paths depending on user preferences and presenting alternative paths to users for selection.Atty Dkt: 11016-8049PCT137. The method of claim 131, further comprising implementing progressive clarification where initial questions address high-level aspects of prompts and subsequent questions drill down into details based on earlier responses.

138. The method of claim 131, further comprising learning from historical interactions, identifying questions that users frequently skip or questions that consistently receive same responses, and adjusting question generation to prioritize valuable questions.

139. The method of claim 131, further comprising reviewing, by the validation system, clarifications provided by the user, identifying aspects of user intent that the response should address, generating questions assessing whether the response adequately addresses those aspects, and prompting for model revision if deficiencies are detected.

140. The method of claim 131, further comprising avoiding overwhelming users with too many questions simultaneously by implementing progressive clarification and enabling users to skip questions if defaults are acceptable.MODEL SMART ROUTER141. A model smart router system for intelligently directing prompts to optimal sets of models comprising: a cache for repetitive requests configured to store previously computed responses to prompts, enabling rapid response when identical or substantially similar prompts are submitted again; a local store configured to optimize utilization of limited context windows in artificial intelligence models by maintaining supplemental storage of information relevant to ongoing conversations or tasks; adaptive learning capabilities configmed to enable the model smart router to improve routing decisions and response quality over time through analysis of historical performance data; a unified model access system configured to enable issuance of single prompts to multiple artificial intelligence models simultaneously, with responses aggregated and delivered from a most suitable model; a response model consensus ranking system configured to implement a process where multiple models generate responses to user-provided prompts and those responses are evaluated by same or different models which rank each response; a model cost tiering system configured to classify artificial intelligence models into cost tiers based on perquery pricing or computational resource requirements; an automated response challenging system configured to ask models challenge questions immediately after models provide responses to validate answers and reduce hallucination; and a response processing sophistication system configured to control number and quality of models used during response generation.

142. The system of claim 141, wherein the cache computes hash values or embeddings for incoming prompts, compares computed values against values for cached prompts to identify matches, and returns cached responses when matches are found, bypassing model invocation.

143. The system of claim 141, wherein the cache implements cache invalidation policies that remove cached responses after specified time periods, when underlying data used to generate responses changes, or when newer model versions become available.

144. The system of claim 141, wherein the cache implements partial matching that identifies prompts similar but not identical to cached prompts, retrieves cached responses as starting points, and invokes models to refine cached responses to address differences.Atty Dkt: 11016-8049PCT145. The system of claim 141, wherein when conversations span multiple turns and accumulated context exceeds model context window limits, the local store maintains full conversation history and selectively loads relevant portions into model context windows based on current prompt focus.

146. The system of claim 141, wherein the local store implements intelligent context selection using relevance scoring that ranks historical messages or information segments by relevance to current prompts.

147. The system of claim 141, wherein the adaptive learning capabilities are further configured to track which models were selected for prompts, how models performed, what response quality scores were achieved, what costs were incurred, and what user satisfaction ratings resulted.

148. The system of claim 141, wherein the adaptive learning capabilities analyze patterns in tracking data, identify correlations between prompt characteristics and model performance, and update prompt historical ranked models data with learned associations.

149. The system of claim 141, wherein the unified model access system broadcasts prompts to sets of models selected by the model smart router, collects responses from all models in parallel, evaluates responses using consensus ranking, selects best responses, and returns selected responses to users.

150. The system of claim 141, wherein the response model consensus ranking system invokes a set of evaluator models with prompts that include an original user prompt and the set of responses generated by response models, requests evaluator models to score each response on dimensions including accuracy, relevance, clarity, completeness, and conciseness, and aggregates scores across evaluator models.

151. A method for intelligently routing prompts to artificial intelligence models comprising: storing, by a cache, previously computed responses to prompts and returning cached responses when identical or substantially similar prompts are submitted again; maintaining, by a local store, full conversation history when conversations span multiple turns and selectively loading relevant portions into model context windows based on current prompt focus; improving, by adaptive learning capabilities, routing decisions over time by tracking which models were selected for prompts, how models performed, and analyzing patterns in tracking data; enabling, by a unified model access system, issuance of single prompts to multiple artificial intelligence models simultaneously; implementing, by a response model consensus ranking system, a process where multiple models generate responses and those responses are evaluated by evaluator models which rank each response; classifying, by a model cost tiering system, artificial intelligence models into cost tiers based on per-query pricing; asking, by an automated response challenging system, models challenge questions immediately after models provide responses to validate answers; and controlling, by a response processing sophistication system, number and quality of models used during response generation.

152. The method of claim 151, further comprising computing, by the cache, hash values or embeddings for incoming prompts, comparing computed values against values for cached prompts to identify matches, and returning cached responses when matches are found.

153. The method of claim 151, further comprising implementing, by the cache, cache invalidation policies that remove cached responses after specified time periods or when underlying data used to generate responses changes.Atty Dkt: 11016-8049PCT154. The method of claim 151, further comprising implementing, by the cache, partial matching that identifies prompts similar but not identical to cached prompts, retrieves cached responses as starting points, and invokes models to refine cached responses.

155. The method of claim 151, further comprising implementing, by the local store, intelligent context selection using relevance scoring that ranks historical messages or information segments by relevance to current prompts, ensuring most pertinent information occupies limited context window space.

156. The method of claim 151, further comprising implementing, by the local store, context summarization where long conversations are periodically summarized by artificial intelligence models, summaries replace detailed histories in context windows, and detailed histories remain available for retrieval if needed.

157. The method of claim 151, further comprising identifying, by the adaptive learning capabilities, correlations between prompt characteristics and model performance, updating prompt historical ranked models data with learned associations, and adjusting model selection logic to favor models that have demonstrated strong performance.

158. The method of claim 151, further comprising broadcasting, by the unified model access system, prompts to sets of models, collecting responses from all models in parallel, evaluating responses using consensus ranking, and selecting best responses.

159. The method of claim 151, further comprising defining, by the model cost tiering system, tier levels including Level 1 for cheap models, Level 2 for economical models, Level 3 for moderate models, and Level 4 for premium models, and enabling users to specify preferred cost tiers through configuration settings.

160. The method of claim 151, further comprising generating, by the automated response challenging system, challenge questions based on prompt challenge data created during prompt pre-processing, submitting challenge questions to models, and comparing challenge responses against original responses to identify inconsistencies.PROMPT PROCESSING161. An artificial intelligence system comprising: a prompt processing component configured to: receive a user input comprising natural language data; extract metadata from the user input, the metadata comprising at least one of: semantic elements, temporal attributes, and quantitative metrics characterizing processing requirements; decompose the user input into a plurality of interconnected sub-components based on the metadata; and determine a processing sequence for the plurality of sub-components, wherein the processing sequence comprises at least one of sequential processing and concurrent processing; a model access component configured to transmit at least one sub-component to at least one artificial intelligence model for processing; and a response aggregation component configured to combine outputs from processing the plurality of subcomponents.

162. The system of claim 161, wherein the metadata comprises semantic elements identifying key terms within the user input.

163. The system of claim 161, wherein the metadata comprises temporal attributes indicating at least one of past, present, and future temporal reference.Atty Dkt: 11016-8049PCT164. The system of claim 161, wherein the quantitative metrics comprise a complexity level assigned on a numerical scale.

165. The system of claim 161, wherein the prompt processing component is further configured to separate content data from query data within the user input.

166. The system of claim 161, wherein the prompt processing component is further configured to identify a content type of the user input, wherein the content type comprises at least one of textual data, visual data, graphical data, and document data.

167. The system of claim 161, further comprising a sophistication control configured to adjust a number of artificial intelligence models used in processing based on a processing parameter.

168. The system of claim 167, wherein the sophistication control provides at least three processing levels corresponding to different quantities of artificial intelligence models.

169. The system of claim 161, wherein the prompt processing component is further configured to generate verification questions associated with the user input and obtain user responses to the verification questions prior to transmitting to the at least one artificial intelligence model.

170. The system of claim 161, wherein the response aggregation component is configmed to: receive a plurality of outputs from a plurality of artificial intelligence models; generate verification data configured to test accuracy of the plurality of outputs; and select an output from the plurality of outputs based on evaluation using the verification data.

171. A method for processing user inputs in an artificial intelligence system, the method comprising: receiving, by a processing component, a user input comprising natural language data; extracting, by the processing component, metadata from the user input, the metadata comprising at least one of: semantic elements, temporal attributes, and quantitative metrics characterizing processing requirements; decomposing, by the processing component, the user input into a plurality of interconnected subcomponents based on the metadata; determining, by the processing component, a processing sequence for the plurality of sub-components, wherein the processing sequence comprises at least one of sequential processing and concurrent processing; transmitting, by a model access component, at least one sub-component to at least one artificial intelligence model for processing; and combining, by a response aggregation component, outputs from processing the plurality of subcomponents.

172. The method of claim 171, further comprising assigning a complexity level to the user input based on at least one of: linguistic characteristics, computational intensity, and resource requirements.

173. The method of claim 171, further comprising parsing the user input to identify and separate content data from query data.

174. The method of claim 171, wherein decomposing the user input comprises generating a plurality of sub-queries, wherein each sub-query is associated with at least one other sub-query through a dependency relationship.

175. The method of claim 174, further comprising processing sub-queries having no dependency relationships concurrently.

176. The method of claim 171, further comprising: generating a sample prompt configmed to extract the metadata from the user input;Atty Dkt: 11016-8049PCT transmitting the sample prompt and the user input to a metadata extraction model; and receiving the metadata from the metadata extraction model.

177. The method of claim 171, further comprising adjusting a number of artificial intelligence models used in processing based on at least one of: a user setting, the quantitative metrics, and a resource constraint.

178. The method of claim 171, further comprising: generating verification questions configmed to validate assumptions regarding the user input; presenting the verification questions to a user; receiving user responses to the verification questions; and modifying the plurality of sub-components based on the user responses.

179. The method of claim 171, further comprising: receiving a plurality of outputs from a plurality of artificial intelligence models processing the at least one sub-component; generating challenge data configured to evaluate the plurality of outputs; and ranking the plurality of outputs based on evaluation criteria applied using the challenge data.

180. The method of claim 179, wherein the evaluation criteria comprise at least one of: accuracy, clarity, conciseness, and completeness.SYSTEM SUBSYSTEMS AND CONFIGURATION181. An artificial intelligence platform system comprising: a prompt pre-processor comprising: a settings optimizer configured to automatically select configuration parameters based on at least one of: characteristics of an input, resource constraints, and user preferences; a repetition identifier configured to compare the input with historical inputs to identify processing optimization opportunities; a knowledge-graph retrieval component configured to perform dynamic knowledge integration combining private data sources and public data sources; and an autonomous agent creator configured to generate specialized processing entities for handling specific portions of the input; a system management infrastructure comprising: a collaboration system enabling multi-user interaction; a financial management system automating resource accounting; and an analytics system tracking performance indicators; a security framework comprising protective measures configured to safeguard data and control access; and a model integration component configmed to provide connectivity to a plurality of external artificial intelligence models.

182. The system of claim 181, wherein the knowledge-graph retrieval component is configured to: receive data in a standardized format; generate a knowledge representation structure from the data; and perform hybrid processing comprising semantic processing and lexical processing.

183. The system of claim 182, wherein the knowledge-graph retrieval component is configured to dynamically generate the knowledge representation structure without requiring pre-defined schema definitions.Atty Dkt: 11016-8049PCT184. The system of claim 181, wherein the prompt pre-processor further comprises a decomposition engine configmed to break down the input into smaller interconnected processing tasks.

185. The system of claim 181, wherein the repetition identifier is configured to: calculate a similarity measure between the input and the historical inputs; identify at least one historical input exceeding a similarity threshold; and retrieve a previously generated response associated with the at least one historical input.

186. The system of claim 181, wherein the autonomous agent creator is configured to: track frequency of agent usage patterns; identify recurring agent configurations; and automatically create and catalog new specialized processing entities based on the recurring agent configmations.

187. The system of claim 181, wherein the system management infrastructure further comprises: a reporting system configured to generate documentation on at least one of: usage, configuration, and financial data; a feedback system configured to collect user evaluation data; and a configuration system enabling adjustment of operational parameters.

188. The system of claim 181, further comprising a data management infrastructure configured to: manage data sources and data flows; store data in at least one database; and provide data through data feeds.

189. The system of claim 181, wherein the model integration component comprises at least one of: an application programming interface, a software development kit, and an integration framework.

190. The system of claim 181, further comprising a microservices architecture enabling independent scaling of functional components.

191. A method for managing an artificial intelligence platform, the method comprising: receiving, by a prompt pre-processor, an input; automatically selecting, by a settings optimizer, configmation parameters based on at least one of: characteristics of the input, resource constraints, and user preferences; comparing, by a repetition identifier, the input with historical inputs to identify processing optimization opportunities; performing, by a knowledge-graph retrieval component, dynamic knowledge integration combining private data sources and public data sources; generating, by an autonomous agent creator, specialized processing entities for handling specific portions of the input; enabling, by a collaboration system, multi-user interaction; tracking, by an analytics system, performance indicators; and providing, by a model integration component, connectivity to a plurality of external artificial intelligence models.

192. The method of claim 191, wherein performing dynamic knowledge integration comprises: receiving data in a standardized format;Atty Dkt: 11016-8049PCT generating a knowledge representation structure from the data without requiring pre-defined schema definitions; performing semantic processing on the data; and performing lexical processing on the data.

193. The method of claim 191, further comprising: calculating a similarity measure between the input and the historical inputs; determining that at least one historical input exceeds a similarity threshold; and providing a response by retrieving a previously generated response associated with the at least one historical input.

194. The method of claim 191, further comprising: tracking frequency of agent usage patterns; identifying recurring agent configurations based on the frequency; and automatically creating and cataloging new specialized processing entities based on the recurring agent configurations.

195. The method of claim 191, further comprising: decomposing the input into a plurality of interconnected processing tasks; and assigning at least one specialized processing entity to handle at least one of the plurality of interconnected processing tasks.

196. The method of claim 191, further comprising: generating documentation on at least one of: usage patterns, configuration data, and financial data; and providing the documentation through a reporting interface.

197. The method of claim 191, further comprising: collecting user evaluation data through a feedback system; and utilizing the user evaluation data to optimize at least one of: model selection, processing parameters, and response quality.

198. The method of claim 191, further comprising managing data sources and data flows using at least one of: an extract-transform-load process, a data pipeline, and a staging system.

199. The method of claim 191, further comprising providing connectivity to the plurality of external artificial intelligence models through at least one of: an application programming interface, a web interface, and a software development kit.

200. The method of claim 191, further comprising independently scaling functional components using a microservices architecture.SECURITY AND PRIVACY201. An artificial intelligence system with security infrastructure comprising: an encryption component configured to apply cryptographic transformation to user inputs and system outputs, wherein cryptographic keys remain exclusively with user clients; an authentication component configmed to require multiple verification factors for access; an access control component configured to manage and restrict access based on roles assigned to users; an audit component configmed to capture and store information related to user activities and system events; andAtty Dkt: 11016-8049PCT a compliance component configured to ensure adherence to data privacy regulations.

202. The system of claim 201, wherein the encryption component is configured to encode information such that only authorized users with correct decryption keys can access the information.

203. The system of claim 201, wherein the authentication component is configured to require at least two verification factors selected from: knowledge factors, possession factors, and inherence factors.

204. The system of claim 201, wherein the access control component is configured to: assign roles to users within an organization; define permissions associated with each role; and enforce the permissions based on the roles.

205. The system of claim 201, wherein the audit component is configured to generate audit trails comprising: timestamps of user activities; identifiers of users performing activities; descriptions of activities performed; and system events associated with the activities.

206. The system of claim 201, wherein the compliance component is configured to ensure adherence to at least one of: General Data Protection Regulation (GDPR), California Consumer Privacy Act (CCPA), and Health Insurance Portability and Accountability Act (HIPAA).

207. The system of claim 201, further comprising an intellectual property protection component configured to safeguard proprietary information processed by the system.

208. The system of claim 201, wherein the encryption component is configured to maintain encryption during data transmission and during data storage.

209. The system of claim 201, further comprising a threat detection component configured to identify security threats based on analysis of the information captured by the audit component.

210. The system of claim 201, wherein the access control component is further configured to implement attributebased access control based on user attributes and environmental conditions.

211. A method for securing an artificial intelligence system, the method comprising: applying, by an encryption component, cryptographic transformation to user inputs and system outputs, wherein cryptographic keys remain exclusively with user clients; requiring, by an authentication component, multiple verification factors for access; managing, by an access control component, access restrictions based on roles assigned to users; capturing, by an audit component, information related to user activities and system events; storing, by the audit component, the information in audit logs; and ensuring, by a compliance component, adherence to data privacy regulations.

212. The method of claim 211, wherein applying cryptographic transformation comprises encoding information such that only authorized users with correct decryption keys can access the information.

213. The method of claim 211, wherein requiring multiple verification factors comprises: requesting a first verification factor comprising at least one of: a password, a PIN, and a security question answer; requesting a second verification factor comprising at least one of: a hardware token, a mobile device confirmation, and a biometric scan; andAtty Dkt: 11016-8049PCT granting access only upon successful verification of both factors.

214. The method of claim 211, wherein managing access restrictions comprises: assigning roles to users within an organization; defining permissions associated with each role; and enforcing the permissions by preventing unauthorized actions.

215. The method of claim 211, wherein capturing information comprises recording: timestamps of user activities; identifiers of users performing activities; descriptions of activities performed; and system events associated with the activities.

216. The method of claim 211, wherein ensuring adherence to data privacy regulations comprises: implementing data handling procedures in compliance with at least one regulation; monitoring system operations for compliance violations; and generating compliance reports.

217. The method of claim 211, further comprising safeguarding proprietary information by restricting access to proprietary data based on user permissions.

218. The method of claim 211, further comprising maintaining cryptographic transformation during data transmission and during data storage.

219. The method of claim 211, further comprising: analyzing the information captured in the audit logs; identifying patterns indicative of security threats; and generating alerts in response to identified security threats.

220. The method of claim 211, further comprising implementing attribute-based access control by evaluating user attributes and environmental conditions prior to granting access.FEEDBACK, ANALYTICS, AND REPORTING221. An artificial intelligence analytics system comprising: a real-time analytics component configured to provide users with concurrent analytics on queries; a query analysis component configured to generate analytic measures related to sets of queries; a performance metrics component configured to determine performance indicators associated with artificial intelligence model operations; a hallucination tracking component configured to measure accuracy metrics of model outputs; a response time monitoring component configured to track latency of model operations; a usage pattern analysis component configured to determine behavioral trends of system utilization; a feedback loop component configured to improve model performance based on at least one of: user interaction data and system metrics; and a reporting component configured to generate reports on usage and billing.

222. The system of claim 221, wherein the real-time analytics component is configured to provide insights for optimizing model interactions and resource allocation.

223. The system of claim 221, wherein the performance metrics component is configured to track which artificial intelligence models perform best for specific types of inputs.Atty Dkt: 11016-8049PCT224. The system of claim 221, wherein the hallucination tracking component is configured to: identify factual inaccuracies in model outputs; quantify a rate of factual inaccuracies; and associate hallucination metrics with specific artificial intelligence models.

225. The system of claim 221, wherein the response time monitoring component is configmed to: measure processing duration for individual queries; calculate average latency for different artificial intelligence models; and utilize latency data as a factor in model selection.

226. The system of claim 221, wherein the usage pattern analysis component is configured to identify: temporal patterns of system usage; user-specific usage characteristics; and model-specific utilization trends.

227. The system of claim 221, wherein the feedback loop component configured to improve model performance comprises: a user feedback mechanism configured to collect explicit user evaluations; and a metrics based feedback mechanism configured to automatically adjust system parameters based on cost metrics, response time metrics, query metrics, performance metrics, hallucination metrics, and sustainability metrics.

228. The system of claim 221, wherein the reporting component is configured to generate detailed reports on at least one of: daily usage, weekly usage, and monthly usage.

229. The system of claim 221, further comprising a usage tracking component configmed to monitor system utilization for billing purposes.

230. The system of claim 221, further comprising a model improvement component configured to refine selection algorithms over time based on the feedback loop component.

231. A method for analytics in an artificial intelligence system, the method comprising: providing, by a real-time analytics component, concurrent analytics on queries to users; generating, by a query analysis component, analytic measures related to sets of queries; determining, by a performance metrics component, performance indicators associated with artificial intelligence model operations; measuring, by a hallucination tracking component, accuracy metrics of model outputs; tracking, by a response time monitoring component, latency of model operations; determining, by a usage pattern analysis component, behavioral trends of system utilization; improving, by a feedback loop component, model performance based on at least one of: user interaction data and system metrics; and generating, by a reporting component, reports on usage and billing.

232. The method of claim 231, wherein providing concurrent analytics comprises providing insights for optimizing model interactions and resource allocation.

233. The method of claim 231, wherein determining performance indicators comprises tracking which artificial intelligence models perform best for specific types of inputs.

234. The method of claim 231, wherein measuring accuracy metrics comprises:Atty Dkt: 11016-8049PCT identifying factual inaccuracies in model outputs; quantifying a rate of factual inaccuracies; and associating hallucination metrics with specific artificial intelligence models.

235. The method of claim 231, wherein tracking latency comprises: measuring processing duration for individual queries; calculating average latency for different artificial intelligence models; and utilizing latency data as a factor in model selection.

236. The method of claim 231, wherein determining behavioral trends comprises identifying: temporal patterns of system usage; user-specific usage characteristics; and model-specific utilization trends.

237. The method of claim 231, wherein improving model performance comprises: collecting explicit user evaluations through a user feedback mechanism; and automatically adjusting system parameters based on cost metrics, response time metrics, query metrics, performance metrics, hallucination metrics, and sustainability metrics.

238. The method of claim 231, wherein generating reports comprises producing detailed reports on at least one of: daily usage, weekly usage, and monthly usage to promote transparency and accountability.

239. The method of claim 231, further comprising: monitoring system utilization for billing purposes; calculating costs based on resource consumption; and automating charging of users based on the costs.

240. The method of claim 231, further comprising refining selection algorithms over time based on accumulated feedback data.SUSTAINABILITY METRICS241. An artificial intelligence system with environmental monitoring comprising: a carbon footprint analytics component configured to determine environmental impact analytics for artificial intelligence operations, wherein the environmental impact analytics comprise monitoring and analyzing CO2 emissions; a computational resource tracking component configmed to track resource consumption of artificial intelligence operations; and a recommendation engine configured to generate strategies for minimizing environmental impact aligned with sustainability goals.

242. The system of claim 241, wherein the carbon footprint analytics component is configured to: monitor CO2 emissions associated with computational operations; calculate a total carbon footprint for a specified time period; and generate environmental impact reports.

243. The system of claim 241, wherein the computational resource tracking component is configured to track at least one of: processing power consumption, energy usage, and infrastructure utilization.

244. The system of claim 241, wherein the recommendation engine is configured to: analyze patterns of resource consumption;Atty Dkt: 11016-8049PCT identify opportunities for resource optimization; and generate recommendations for reducing carbon footprint.

245. The system of claim 241, further comprising a visualization component configured to present carbon footprint data and environmental impact metrics through a user interface.

246. The system of claim 241, wherein the recommendation engine is configured to suggest at least one of: selection of more energy efficient artificial intelligence models; timing of operations to utilize renewable energy sources; and optimization of computational processes to reduce resource consumption.

247. The system of claim 241, further comprising a comparison component configured to compare environmental impact of different artificial intelligence models.

248. The system of claim 241, wherein the system is configured to enable selection of artificial intelligence models based at least in part on environmental impact metrics.

249. The system of claim 241, further comprising a sustainability reporting component configured to generate reports suitable for environmental compliance and corporate sustainability initiatives.

250. The system of claim 241, wherein the carbon footprint analytics component is configured to track environmental impact on a per-query basis.

251. A method for environmental monitoring in an artificial intelligence system, the method comprising: determining, by a carbon footprint analytics component, environmental impact analytics for artificial intelligence operations, wherein the environmental impact analytics comprise monitoring and analyzing CO2 emissions; tracking, by a computational resource tracking component, resource consumption of artificial intelligence operations; and generating, by a recommendation engine, strategies for minimizing environmental impact aligned with sustainability goals.

252. The method of claim 251, wherein determining environmental impact analytics comprises: monitoring CO2 emissions associated with computational operations; calculating a total carbon footprint for a specified time period; and generating environmental impact reports.

253. The method of claim 251, wherein tracking resource consumption comprises tracking at least one of: processing power consumption, energy usage, and infrastructure utilization.

254. The method of claim 251, wherein generating strategies comprises: analyzing patterns of resource consumption; identifying opportunities for resource optimization; and generating recommendations for reducing carbon footprint.

255. The method of claim 251, further comprising presenting carbon footprint data and environmental impact metrics through a user interface.

256. The method of claim 251, wherein generating strategies comprises suggesting at least one of: selection of more energy efficient artificial intelligence models; timing of operations to utilize renewable energy sources; and optimization of computational processes to reduce resource consumption.Atty Dkt: 11016-8049PCT257. The method of claim 251, further comprising comparing environmental impact of different artificial intelligence models to enable environmentally -informed model selection.

258. The method of claim 251, further comprising enabling selection of artificial intelligence models based at least in part on environmental impact metrics.

259. The method of claim 251, further comprising generating reports suitable for environmental compliance and corporate sustainability initiatives.

260. The method of claim 251, further comprising tracking environmental impact on a per-query basis to enable detailed environmental accounting.MODEL IMPROVEMENT ANALYTICS AND METRICS AS A SERVICE261. A system for providing model improvement analytics associated with artificial intelligence model unification, the system comprising: a computing platform configured to provide access to a plurality of artificial intelligence models; an analytics subsystem configured to generate analytic measures related to queries processed by the plurality of artificial intelligence models; and a reporting subsystem configured to generate reports comprising performance metrics associated with the plurality of artificial intelligence models, wherein the performance metrics include at least one of response times, usage patterns, or accuracy indicators.

262. The system of claim 261, wherein the analytic measures include at least one of query analysis data, performance evaluation data, hallucination metrics, or sustainability metrics.

263. The system of claim 261, wherein the reporting subsystem is further configured to generate comparative assessments between different artificial intelligence models of the plurality of artificial intelligence models.

264. The system of claim 263, wherein the comparative assessments include explanations of why one artificial intelligence model outperforms another artificial intelligence model.

265. The system of claim 261, wherein the performance metrics are provided in real-time as queries are processed.

266. The system of claim 261, further comprising a feedback subsystem configmed to provide evaluative information to model developers based on the analytic measures.

267. The system of claim 266, wherein the evaluative information includes detailed data about query types for which each artificial intelligence model performs well and query types for which each artificial intelligence model falls behind in rankings.

268. The system of claim 261, wherein the analytics subsystem is further configured to generate recommendations for product development based on the analytic measures.

269. The system of claim 261, wherein the reporting subsystem is configmed to enforce model competition by providing comparative performance data to users.

270. The system of claim 261, wherein the performance metrics include cost metrics associated with processing queries using each artificial intelligence model of the plurality of artificial intelligence models.

271. A method for providing model improvement analytics associated with artificial intelligence model unification, the method comprising: providing, by a computing platform, access to a plurality of artificial intelligence models; generating, by an analytics subsystem, analytic measures related to queries processed by the plurality of artificial intelligence models; andAtty Dkt: 11016-8049PCT generating, by a reporting subsystem, reports comprising performance metrics associated with the plurality of artificial intelligence models, wherein the performance metrics include at least one of response times, usage patterns, or accuracy indicators.

272. The method of claim 271, wherein generating analytic measures includes determining at least one of query analysis data, performance evaluation data, hallucination metrics, or sustainability metrics.

273. The method of claim 271, further comprising generating comparative assessments between different artificial intelligence models of the plurality of artificial intelligence models.

274. The method of claim 273, wherein generating comparative assessments includes providing explanations of why one artificial intelligence model outperforms another artificial intelligence model.

275. The method of claim 271, wherein the performance metrics are generated at a same time as processing of queries.

276. The method of claim 271, further comprising providing evaluative information to model developers based on the analytic measures.

277. The method of claim 276, wherein providing evaluative information includes providing detailed data about query types for which each artificial intelligence model performs well and query types for which each artificial intelligence model falls behind in rankings.

278. The method of claim 271, further comprising generating recommendations for product development based on the analytic measures.

279. The method of claim 271, further comprising enforcing model competition by providing comparative performance data to users.

280. The method of claim 271, wherein the performance metrics include cost metrics associated with processing queries using each artificial intelligence model of the plurality of artificial intelligence models.SANDBOX AND TESTING ENVIRONMENTS281. A system for testing artificial intelligence model configurations, the system comprising: a computing platform configured to provide access to a plurality of artificial intelligence models; a controlled execution space configured to enable users to validate at least one of input text, system specifications, or system interfaces before operational deployment; and a deployment subsystem configured to migrate validated configurations from the controlled execution space to a production environment.

282. The system of claim 281, wherein the controlled execution space is isolated from the production environment.

283. The system of claim 281, wherein the controlled execution space is configured to enable testing of natural language inputs without affecting operational systems.

284. The system of claim 281, wherein the controlled execution space is configured to enable testing of operational parameters before deployment.

285. The system of claim 281, wherein the controlled execution space is configured to enable testing of API connections before deployment.

286. The system of claim 281, wherein the deployment subsystem is configured to prevent deployment unless validation in the controlled execution space is successful.

287. The system of claim 281, wherein the controlled execution space provides a simulation environment that replicates operational conditions.Atty Dkt: 11016-8049PCT288. The system of claim 281, wherein the controlled execution space is configured to test interactions between multiple artificial intelligence models.

289. The system of claim 281, further comprising a validation subsystem configured to identify potential issues before operational deployment.

290. The system of claim 281, wherein the controlled execution space enables users to test modifications to input text processing without impacting production systems.

291. A method for testing artificial intelligence model configurations, the method comprising: providing, by a computing platform, access to a plurality of artificial intelligence models; enabling, by a controlled execution space, users to validate at least one of input text, system specifications, or system interfaces before operational deployment; and migrating, by a deployment subsystem, validated configurations from the controlled execution space to a production environment.

292. The method of claim 291, wherein the controlled execution space is isolated from the production environment.

293. The method of claim 291, wherein enabling users to validate includes enabling testing of natural language inputs without affecting operational systems.

294. The method of claim 291, wherein enabling users to validate includes enabling testing of operational parameters before deployment.

295. The method of claim 291, wherein enabling users to validate includes enabling testing of API connections before deployment.

296. The method of claim 291, further comprising preventing deployment unless validation in the controlled execution space is successful.

297. The method of claim 291, wherein the controlled execution space provides a simulation environment that replicates operational conditions.

298. The method of claim 291, wherein enabling users to validate includes testing interactions between multiple artificial intelligence models.

299. The method of claim 291, further comprising identifying potential issues before operational deployment.

300. The method of claim 291, wherein enabling users to validate includes testing modifications to input text processing without impacting production systems.COLLABORATION TOOLS301. A system for facilitating multi-user interaction associated with artificial intelligence model unification, the system comprising: a computing platform configured to provide access to a plurality of artificial intelligence models; a set of multi-user interfaces associated with artificial intelligence model unification and access, wherein the multi-user interfaces enable multiple users to jointly work on projects; a set of content repositories associated with artificial intelligence model unification and access, wherein the content repositories store reusable natural language inputs; and a change tracking system associated with artificial intelligence model unification and access, wherein the change tracking system maintains revision history for configurations.

302. The system of claim 301, wherein the multi-user interfaces enable simultaneous access by multiple users to shared project data.Atty Dkt: 11016-8049PCT303. The system of claim 301, wherein the content repositories store template natural language inputs that can be reused across multiple projects.

304. The system of claim 301, wherein the change tracking system maintains a complete history of modifications to natural language inputs.

305. The system of claim 301, wherein the change tracking system enables users to revert to previous versions of configurations.

306. The system of claim 301, wherein the multi-user interfaces include access controls that define permissions for different users.

307. The system of claim 301, wherein the content repositories are organized by project, user, or subject matter category.

308. The system of claim 301, wherein the change tracking system maintains metadata indicating which user made each modification and when the modification was made.

309. The system of claim 301, further comprising a notification subsystem configured to alert users when other users modify shared configmations.

310. The system of claim 301, wherein the multi-user interfaces enable team based workflow management for complex projects involving multiple artificial intelligence models.

311. A method for facilitating multi-user interaction associated with artificial intelligence model unification, the method comprising: providing, by a computing platform, access to a plurality of artificial intelligence models; enabling, through multi-user interfaces, multiple users to jointly work on projects associated with artificial intelligence model unification and access; storing, in content repositories, reusable natural language inputs associated with artificial intelligence model unification and access; and maintaining, by a change tracking system, revision history for configurations associated with artificial intelligence model unification and access.

312. The method of claim 311, wherein enabling multiple users to jointly work includes enabling simultaneous access by multiple users to shared project data.

313. The method of claim 311, wherein storing reusable natural language inputs includes storing template natural language inputs that can be reused across multiple projects.

314. The method of claim 311, wherein maintaining revision history includes maintaining a complete history of modifications to natural language inputs.

315. The method of claim 311, further comprising enabling users to revert to previous versions of configurations.

316. The method of claim 311, further comprising defining access controls that specify permissions for different users.

317. The method of claim 311, wherein storing reusable natural language inputs includes organizing the content repositories by project, user, or subject matter category.

318. The method of claim 311, wherein maintaining revision history includes maintaining metadata indicating which user made each modification and when the modification was made.

319. The method of claim 311, further comprising alerting users when other users modify shared configurations.Atty Dkt: 11016-8049PCT320. The method of claim 311, wherein enabling multiple users to jointly work includes enabling team based workflow management for complex projects involving multiple artificial intelligence models.COST MANAGEMENT321. A system for managing resource consumption associated with artificial intelligence model unification, the system comprising: a computing platform configured to provide access to a plurality of artificial intelligence models, wherein each artificial intelligence model is associated with a cost per query; a financial control subsystem configmed to: set temporal budget constraints for users; and track cumulative costs incurred by each user during a time period; a quality control subsystem configured to define multiple service levels for model selection, wherein each service level is associated with a different cost range; and a processing subsystem configured to select one or more artificial intelligence models from the plurality of artificial intelligence models based on the service level defined for a user and remaining budget within the temporal budget constraints.

322. The system of claim 321, wherein the temporal budget constraints comprise daily allocation limits that reset at predetermined times.

323. The system of claim 322, wherein the financial control subsystem is further configmed to prevent users from submitting additional queries when the daily allocation limit is reached unless the user modifies the limit or activates a manual override.

324. The system of claim 323, wherein the financial control subsystem is configured to maintain a log recording when manual overrides me activated and which users activated them.

325. The system of claim 321, wherein the multiple service levels comprise at least four tiers ranging from low-cost to high cost artificial intelligence models.

326. The system of claim 325, wherein the four tiers comprise: a first tier associated with low-cost artificial intelligence models; a second tier associated with economical artificial intelligence models; a third tier associated with moderate-cost artificial intelligence models; and a fourth tier associated with premium artificial intelligence models.

327. The system of claim 321, wherein the quality control subsystem is further configured to define separate service levels for input processing and output processing.

328. The system of claim 327, wherein the processing subsystem is configured to select a first set of artificial intelligence models for input processing based on a first service level and a second set of artificial intelligence models for output processing based on a second service level.

329. The system of claim 321, wherein the processing subsystem dynamically adjusts model selection based on cumulative costs already incurred to avoid exceeding the temporal budget constraints.

330. The system of claim 321, further comprising a reporting subsystem configured to provide users with transparency regarding costs incurred for each query.

331. A method for managing resource consumption associated with artificial intelligence model unification, the method comprising:Atty Dkt: 11016-8049PCT providing, by a computing platform, access to a plurality of artificial intelligence models, wherein each artificial intelligence model is associated with a cost per query; setting, by a financial control subsystem, temporal budget constraints for users; tracking, by the financial control subsystem, cumulative costs incurred by each user during a time period; defining, by a quality control subsystem, multiple service levels for model selection, wherein each service level is associated with a different cost range; and selecting, by a processing subsystem, one or more artificial intelligence models from the plurality of artificial intelligence models based on the service level defined for a user and remaining budget within the temporal budget constraints.

332. The method of claim 331, wherein the temporal budget constraints comprise daily allocation limits that reset at predetermined times.

333. The method of claim 332, further comprising preventing users from submitting additional queries when the daily allocation limit is reached unless the user modifies the limit or activates a manual override.

334. The method of claim 333, further comprising maintaining a log recording when manual overrides are activated and which users activated them.

335. The method of claim 331, wherein the multiple service levels comprise at least four tiers ranging from low-cost to high cost artificial intelligence models.

336. The method of claim 335, wherein the four tiers comprise: a first tier associated with low-cost artificial intelligence models; a second tier associated with economical artificial intelligence models; a third tier associated with moderate-cost artificial intelligence models; and a fourth tier associated with premium artificial intelligence models.

337. The method of claim 331, wherein defining multiple service levels includes defining separate service levels for input processing and output processing.

338. The method of claim 337, wherein selecting one or more artificial intelligence models includes selecting a first set of artificial intelligence models for input processing based on a first service level and a second set of artificial intelligence models for output processing based on a second service level.

339. The method of claim 331, wherein selecting one or more artificial intelligence models includes dynamically adjusting model selection based on cumulative costs already incurred to avoid exceeding the temporal budget constraints.

340. The method of claim 331, further comprising providing users with transparency regarding costs incurred for each query.Automation of Response Challenging and Feedback341. A system for automated output verification associated with artificial intelligence model unification, the system comprising: a computing platform configured to provide access to a plurality of artificial intelligence models; an input processing subsystem configured to receive a user input and process the user input to generate processed input data; a model selection subsystem configured to select one or more artificial intelligence models from the plurality of artificial intelligence models to process the processed input data;Atty Dkt: 11016-8049PCT a response generation subsystem configured to generate one or more responses by providing the processed input data to the one or more artificial intelligence models; and a validation subsystem configured to: generate verification inputs based on the user input and the one or more responses; and provide the verification inputs to at least one artificial intelligence model to validate accuracy of the one or more responses.

342. The system of claim 341, wherein the validation subsystem is configured to generate the verification inputs immediately after receiving the one or more responses.

343. The system of claim 341, wherein the verification inputs comprise challenge questions configured to test whether the one or more responses contain fabricated information.

344. The system of claim 341, wherein the validation subsystem is configured to iteratively improve the one or more responses based on outputs from the at least one artificial intelligence model in response to the verification inputs.

345. The system of claim 341, wherein the input processing subsystem is configured to: decompose the user input into a plurality of component inputs; and determine a sequence for processing the plurality of component inputs.

346. The system of claim 345, wherein the validation subsystem is further configured to generate verification inputs to validate the sequence for processing the plurality of component inputs.

347. The system of claim 346, wherein the verification inputs for validating the sequence include questions about whether the plurality of component inputs are in proper sequence and whether the plurality of component inputs are comprehensive.

348. The system of claim 341, wherein the model selection subsystem is configmed to select different artificial intelligence models for generating responses and for validating responses.

349. The system of claim 341, wherein the response generation subsystem is configured to generate multiple responses using multiple artificial intelligence models, and wherein the validation subsystem is configured to rank the multiple responses based on quality criteria.

350. The system of claim 349, wherein the quality criteria comprise at least two of accuracy, clarity, or conciseness.

351. A method for automated output verification associated with artificial intelligence model unification, the method comprising: providing, by a computing platform, access to a plurality of artificial intelligence models; receiving, by an input processing subsystem, a user input; processing, by the input processing subsystem, the user input to generate processed input data; selecting, by a model selection subsystem, one or more artificial intelligence models from the plurality of artificial intelligence models to process the processed input data; generating, by a response generation subsystem, one or more responses by providing the processed input data to the one or more artificial intelligence models; generating, by a validation subsystem, verification inputs based on the user input and the one or more responses; and providing, by the validation subsystem, the verification inputs to at least one artificial intelligence model to validate accuracy of the one or more responses.Atty Dkt: 11016-8049PCT352. The method of claim 351, wherein generating verification inputs occurs immediately after generating the one or more responses.

353. The method of claim 351, wherein the verification inputs comprise challenge questions configured to test whether the one or more responses contain fabricated information.

354. The method of claim 351, further comprising iteratively improving the one or more responses based on outputs from the at least one artificial intelligence model in response to the verification inputs.

355. The method of claim 351, wherein processing the user input includes: decomposing the user input into a plurality of component inputs; and determining a sequence for processing the plurality of component inputs.

356. The method of claim 355, further comprising generating verification inputs to validate the sequence for processing the plurality of component inputs.

357. The method of claim 356, wherein the verification inputs for validating the sequence include questions about whether the plurality of component inputs are in proper sequence and whether the plurality of component inputs are comprehensive.

358. The method of claim 351, wherein selecting one or more artificial intelligence models includes selecting different artificial intelligence models for generating responses and for validating responses.

359. The method of claim 351, wherein generating one or more responses includes generating multiple responses using multiple artificial intelligence models, and further comprising ranking the multiple responses based on quality criteria.

360. The method of claim 359, wherein the quality criteria comprise at least two of accuracy, clarity, or conciseness. Asynchronous Management of Prompts and Responses361. A system for concurrent processing of multiple inputs associated with artificial intelligence model unification, the system comprising: a computing platform configured to provide access to a plurality of artificial intelligence models; an input management subsystem configured to receive a plurality of user inputs and manage the plurality of user inputs in a non-blocking manner; a processing subsystem configured to distribute the plurality of user inputs to the plurality of artificial intelligence models such that multiple user inputs are processed in parallel; and a response management subsystem configured to collect responses from the plurality of artificial intelligence models and associate each response with a corresponding user input without requiring sequential processing.

362. The system of claim 361, wherein the input management subsystem is configured to queue incoming user inputs and dispatch them to available artificial intelligence models as processing capacity becomes available.

363. The system of claim 361, wherein the processing subsystem is configured to send different portions of a single user input to different artificial intelligence models concurrently.

364. The system of claim 361, wherein the response management subsystem is configmed to return responses to users as they become available rather than waiting for all responses to be generated.

365. The system of claim 361, further comprising a coordination subsystem configured to manage dependencies between related user inputs while allowing independent user inputs to be processed in parallel.Atty Dkt: 11016-8049PCT366. The system of claim 361, wherein the system is configmed to handle large-scale deployment involving thousands of concurrent user inputs without compromising performance.

367. The system of claim 361, wherein the processing subsystem is configured to dynamically allocate processing resources based on current system load.

368. The system of claim 361, wherein the input management subsystem is configured to assign priorities to user inputs and process higher priority inputs before lower priority inputs.

369. The system of claim 361, wherein the response management subsystem is configured to aggregate multiple partial responses into a final response for a user.

370. The system of claim 361, wherein the system is configmed to process decomposed components of a user input in parallel when the components me independent of each other.

371. A method for concurrent processing of multiple inputs associated with artificial intelligence model unification, the method comprising: providing, by a computing platform, access to a plurality of artificial intelligence models; receiving, by an input management subsystem, a plurality of user inputs; managing, by the input management subsystem, the plurality of user inputs in a non-blocking manner; distributing, by a processing subsystem, the plurality of user inputs to the plurality of artificial intelligence models such that multiple user inputs me processed in parallel; and collecting, by a response management subsystem, responses from the plurality of artificial intelligence models and associating each response with a corresponding user input without requiring sequential processing.

372. The method of claim 371, wherein managing the plurality of user inputs includes queuing incoming user inputs and dispatching them to available artificial intelligence models as processing capacity becomes available.

373. The method of claim 371, wherein distributing the plurality of user inputs includes sending different portions of a single user input to different artificial intelligence models concurrently.

374. The method of claim 371, further comprising returning responses to users as they become available rather than waiting for all responses to be generated.

375. The method of claim 371, further comprising managing dependencies between related user inputs while allowing independent user inputs to be processed in parallel.

376. The method of claim 371, further comprising handling large-scale deployment involving thousands of concurrent user inputs without compromising performance.

377. The method of claim 371, further comprising dynamically allocating processing resources based on current system load.

378. The method of claim 371, wherein managing the plurality of user inputs includes assigning priorities to user inputs and processing higher priority inputs before lower priority inputs.

379. The method of claim 371, further comprising aggregating multiple partial responses into a final response for a user.

380. The method of claim 371, further comprising processing decomposed components of a user input in parallel when the components are independent of each other.Operational, Strategic, Decision, and Supervised Autonomy Workflows381. A system for managing automated workflows associated with artificial intelligence model unification, the system comprising:Atty Dkt: 11016-8049PCT a computing platform configured to provide access to a plurality of artificial intelligence models; a workflow definition subsystem configured to enable users to design process sequences involving one or more artificial intelligence models; a workflow configuration subsystem configured to set operational parameters for the process sequences; a workflow execution subsystem configured to automatically execute the process sequences based on triggering events; and a workflow tracking subsystem configured to monitor execution of the process sequences and record workflow states.

382. The system of claim 381, wherein the process sequences comprise at least one of operational workflows, strategic workflows, decision workflows, or supervised autonomy workflows.

383. The system of claim 382, wherein supervised autonomy workflows comprise workflows that include human approval steps at predetermined points during automated execution.

384. The system of claim 381, wherein the workflow definition subsystem is configured to enable users to specify conditional branching based on outputs from artificial intelligence models.

385. The system of claim 381, wherein the workflow execution subsystem is configured to execute workflows in response to scheduled triggers or event based triggers.

386. The system of claim 381, wherein the workflow tracking subsystem is configured to generate alerts when workflows encounter errors or require human intervention.

387. The system of claim 381, wherein the workflow configuration subsystem is configured to set resource allocation limits for workflows.

388. The system of claim 381, wherein the workflow definition subsystem enables users to define multi-step workflows that involve sequential processing by multiple different artificial intelligence models.

389. The system of claim 381, further comprising a workflow optimization subsystem configured to analyze workflow performance and recommend improvements.

390. The system of claim 381, wherein the workflow execution subsystem is configured to handle both synchronous workflows and asynchronous workflows.

391. A method for managing automated workflows associated with artificial intelligence model unification, the method comprising: providing, by a computing platform, access to a plurality of artificial intelligence models; enabling, by a workflow definition subsystem, users to design process sequences involving one or more artificial intelligence models; setting, by a workflow configuration subsystem, operational parameters for the process sequences; automatically executing, by a workflow execution subsystem, the process sequences based on triggering events; and monitoring, by a workflow tracking subsystem, execution of the process sequences and recording workflow states.

392. The method of claim 391, wherein the process sequences comprise at least one of operational workflows, strategic workflows, decision workflows, or supervised autonomy workflows.

393. The method of claim 392, wherein supervised autonomy workflows comprise workflows that include human approval steps at predetermined points during automated execution.Atty Dkt: 11016-8049PCT394. The method of claim 391, wherein enabling users to design process sequences includes enabling users to specify conditional branching based on outputs from artificial intelligence models.

395. The method of claim 391, wherein automatically executing the process sequences includes executing workflows in response to scheduled triggers or event based triggers.

396. The method of claim 391, further comprising generating alerts when workflows encounter errors or require human intervention.

397. The method of claim 391, wherein setting operational parameters includes setting resource allocation limits for workflows.

398. The method of claim 391, wherein enabling users to design process sequences includes enabling users to define multi-step workflows that involve sequential processing by multiple different artificial intelligence models.

399. The method of claim 391, further comprising analyzing workflow performance and recommending improvements.

400. The method of claim 391, wherein automatically executing the process sequences includes handling both synchronous workflows and asynchronous workflows.Prompt Pre-Processing and Metadata Calculation401. A system for input transformation associated with artificial intelligence model unification, the system comprising: a computing platform configured to provide access to a plurality of artificial intelligence models; a metadata extraction subsystem configured to analyze a user input and extract descriptive data comprising at least one of semantic indicators, temporal characteristics, or processing characteristics; a complexity evaluation subsystem configured to determine a difficulty level of the user input based on the descriptive data; a settings determination subsystem configured to select processing parameters for the user input based on the difficulty level; and an input processing subsystem configured to process the user input using the processing parameters.

402. The system of claim 401, wherein the semantic indicators comprise keywords extracted from the user input.

403. The system of claim 401, wherein the temporal characteristics comprise an indication of whether the user input relates to past events, present events, or future events.

404. The system of claim 401, wherein the processing characteristics comprise a complexity score on a numerical scale.

405. The system of claim 404, wherein the complexity score ranges from 1 to 5, with 1 representing low difficulty and 5 representing high difficulty.

406. The system of claim 401, wherein the settings determination subsystem is configmed to select at least one of: whether to decompose the user input, which artificial intelligence models to use, or how many artificial intelligence models to use.

407. The system of claim 401, further comprising a user interaction subsystem configured to query the user for additional information when the descriptive data indicates ambiguity in the user input.

408. The system of claim 401, further comprising a redundancy detection subsystem configured to identify repetitive content in the user input and modify the user input to remove the repetitive content.Atty Dkt: 11016-8049PCT409. The system of claim 401, further comprising a data retrieval subsystem configured to retrieve contextual information relevant to the user input from external data sources.

410. The system of claim 401, wherein the metadata extraction subsystem is configured to identify whether the user input includes data to be processed, questions to be answered, or both.

411. A method for input transformation associated with artificial intelligence model unification, the method comprising: providing, by a computing platform, access to a plurality of artificial intelligence models; analyzing, by a metadata extraction subsystem, a user input; extracting, by the metadata extraction subsystem, descriptive data comprising at least one of semantic indicators, temporal characteristics, or processing characteristics; determining, by a complexity evaluation subsystem, a difficulty level of the user input based on the descriptive data; selecting, by a settings determination subsystem, processing parameters for the user input based on the difficulty level; and processing, by an input processing subsystem, the user input using the processing parameters.

412. The method of claim 411, wherein the semantic indicators comprise keywords extracted from the user input.

413. The method of claim 411, wherein the temporal characteristics comprise an indication of whether the user input relates to past events, present events, or future events.

414. The method of claim 411, wherein the processing characteristics comprise a complexity score on a numerical scale.

415. The method of claim 414, wherein the complexity score ranges from 1 to 5, with 1 representing low difficulty and 5 representing high difficulty.

416. The method of claim 411, wherein selecting processing parameters includes selecting at least one of: whether to decompose the user input, which artificial intelligence models to use, or how many artificial intelligence models to use.

417. The method of claim 411, further comprising querying the user for additional information when the descriptive data indicates ambiguity in the user input.

418. The method of claim 411, further comprising identifying repetitive content in the user input and modifying the user input to remove the repetitive content.

419. The method of claim 411, further comprising retrieving contextual information relevant to the user input from external data sources.

420. The method of claim 411, wherein extracting descriptive data includes identifying whether the user input includes data to be processed, questions to be answered, or both.Settings Matrix and Processing Settings421. A system for dynamic configuration selection associated with artificial intelligence model unification, the system comprising: a computing platform configured to provide access to a plurality of artificial intelligence models; an input analysis subsystem configured to determine characteristics of a user input; a configuration mapping subsystem comprising a decision framework that maps combinations of input characteristics and user defined preferences to processing configurations; andAtty Dkt: 11016-8049PCT a processing subsystem configured to process the user input according to a processing configuration selected from the decision framework based on the characteristics of the user input and the user defined preferences.

422. The system of claim 421, wherein the input characteristics comprise a difficulty level of the user input.

423. The system of claim 421, wherein the user defined preferences comprise a cost tier selection indicating a preferred balance between cost and quality.

424. The system of claim 421, wherein the user defined preferences comprise a processing sophistication setting indicating a level of processing complexity to apply.

425. The system of claim 421, wherein the processing configuration comprises settings for at least one of: whether to decompose the user input, whether to validate outputs, which artificial intelligence models to use, or how many artificial intelligence models to use.

426. The system of claim 421, wherein the decision framework comprises a matrix with multiple dimensions including input difficulty level, cost tier, and processing sophistication level.

427. The system of claim 421, wherein the processing configuration comprises separate settings for input processing and output processing.

428. The system of claim 427, wherein the configuration mapping subsystem uses a first algorithm to determine input processing settings and a second algorithm to determine output processing settings.

429. The system of claim 421, wherein the decision framework is configured to prioritize cost constraints over quality when temporal budget limits are being approached.

430. The system of claim 421, further comprising a learning subsystem configured to update the decision framework based on historical performance data.

431. A method for dynamic configuration selection associated with artificial intelligence model unification, the method comprising: providing, by a computing platform, access to a plurality of artificial intelligence models; determining, by an input analysis subsystem, characteristics of a user input; accessing, by a configuration mapping subsystem, a decision framework that maps combinations of input characteristics and user defined preferences to processing configurations; selecting, by the configuration mapping subsystem, a processing configuration from the decision framework based on the characteristics of the user input and the user defined preferences; and processing, by a processing subsystem, the user input according to the selected processing configmation.

432. The method of claim 431, wherein the input characteristics comprise a difficulty level of the user input.

433. The method of claim 431, wherein the user defined preferences comprise a cost tier selection indicating a preferred balance between cost and quality.

434. The method of claim 431, wherein the user defined preferences comprise a processing sophistication setting indicating a level of processing complexity to apply.

435. The method of claim 431, wherein the processing configuration comprises settings for at least one of: whether to decompose the user input, whether to validate outputs, which artificial intelligence models to use, or how many artificial intelligence models to use.

436. The method of claim 431, wherein the decision framework comprises a matrix with multiple dimensions including input difficulty level, cost tier, and processing sophistication level.Atty Dkt: 11016-8049PCT437. The method of claim 431, wherein the processing configuration comprises separate settings for input processing and output processing.

438. The method of claim 437, further comprising using a first algorithm to determine input processing settings and a second algorithm to determine output processing settings.

439. The method of claim 431, further comprising prioritizing cost constraints over quality when temporal budget limits are being approached.

440. The method of claim 431, further comprising updating the decision framework based on historical performance data.Prompt Decomposition, Chaining, and Consensus Ranking441. A system for query decomposition and sequential processing associated with artificial intelligence model unification, the system comprising: a computing platform configmed to provide access to a plurality of artificial intelligence models; a decomposition subsystem configured to: analyze a user input to identify multiple component tasks; and generate a plurality of component inputs, each component input corresponding to one of the multiple component tasks; a sequencing subsystem configured to determine a processing order for the plurality of component inputs based on dependencies between the component tasks; a processing subsystem configured to process the plurality of component inputs according to the processing order using one or more artificial intelligence models; and an aggregation subsystem configured to combine outputs from processing the plurality of component inputs into a final output.

442. The system of claim 441, wherein the decomposition subsystem is configured to use at least one artificial intelligence model from the plurality of artificial intelligence models to identify the multiple component tasks.

443. The system of claim 441, wherein the sequencing subsystem is configured to determine whether component inputs should be processed sequentially or in parallel based on whether dependencies exist between them.

444. The system of claim 441, further comprising a validation subsystem configured to verify that the plurality of component inputs collectively address all aspects of the user input.

445. The system of claim 444, wherein the validation subsystem is configured to generate verification inputs that ask whether the plurality of component inputs are in proper sequence and whether they are comprehensive.

446. The system of claim 441, wherein the processing subsystem is configured to send multiple component inputs to a single artificial intelligence model as a bundled input to reduce API calls.

447. The system of claim 441, wherein the processing subsystem is configured to send different component inputs to different artificial intelligence models based on model specializations.

448. The system of claim 441, further comprising a consensus subsystem configured to: generate multiple alternative decompositions of the user input using multiple artificial intelligence models; evaluate quality of each alternative decomposition; and select a highest-quality decomposition for processing.

449. The system of claim 448, wherein evaluating quality includes using the multiple artificial intelligence models to rank the alternative decompositions.Atty Dkt: 11016-8049PCT450. The system of claim 441, wherein the decomposition subsystem is configured to break down complex user inputs that exceed context window limits of individual artificial intelligence models.

451. A method for query decomposition and sequential processing associated with artificial intelligence model unification, the method comprising: providing, by a computing platform, access to a plurality of artificial intelligence models; analyzing, by a decomposition subsystem, a user input to identify multiple component tasks; generating, by the decomposition subsystem, a plurality of component inputs, each component input corresponding to one of the multiple component tasks; determining, by a sequencing subsystem, a processing order for the plurality of component inputs based on dependencies between the component tasks; processing, by a processing subsystem, the plurality of component inputs according to the processing order using one or more artificial intelligence models; and combining, by an aggregation subsystem, outputs from processing the plurality of component inputs into a final output.

452. The method of claim 451, wherein analyzing the user input includes using at least one artificial intelligence model from the plurality of artificial intelligence models to identify the multiple component tasks.

453. The method of claim 451, wherein determining the processing order includes determining whether component inputs should be processed sequentially or in parallel based on whether dependencies exist between them.

454. The method of claim 451, further comprising verifying that the plurality of component inputs collectively address all aspects of the user input.

455. The method of claim 454, wherein verifying includes generating verification inputs that ask whether the plurality of component inputs are in proper sequence and whether they are comprehensive.

456. The method of claim 451, wherein processing the plurality of component inputs includes sending multiple component inputs to a single artificial intelligence model as a bundled input to reduce API calls.

457. The method of claim 451, wherein processing the plurality of component inputs includes sending different component inputs to different artificial intelligence models based on model specializations.

458. The method of claim 451, further comprising: generating multiple alternative decompositions of the user input using multiple artificial intelligence models; evaluating quality of each alternative decomposition; and selecting a highest-quality decomposition for processing.

459. The method of claim 458, wherein evaluating quality includes using the multiple artificial intelligence models to rank the alternative decompositions.

460. The method of claim 451, wherein analyzing the user input includes breaking down complex user inputs that exceed context window limits of individual artificial intelligence models.Model Smart Router (MSR) Processing461. A system for intelligent model selection associated with artificial intelligence model unification, the system comprising: a computing platform configured to provide access to a plurality of artificial intelligence models;Atty Dkt: 11016-8049PCT a performance tracking subsystem configured to maintain historical performance data for the plurality of artificial intelligence models across different query types; a routing subsystem configured to: analyze characteristics of a user input; identify one or more artificial intelligence models from the plurality of artificial intelligence models that are predicted to perform optimally for the user input based on the historical performance data and the characteristics of the user input; and direct the user input to the identified one or more artificial intelligence models; and a feedback subsystem configured to update the historical performance data based on quality of outputs generated in response to the user input.

462. The system of claim 461, wherein the characteristics of the user input comprise at least one of: subject matter, temporal context, geographic context, or complexity level.

463. The system of claim 461, wherein the routing subsystem is configured to select different artificial intelligence models for different users based on user-specific preferences or permissions.

464. The system of claim 461, wherein the routing subsystem is configured to dynamically switch between artificial intelligence models during processing of a single user input based on intermediate results.

465. The system of claim 461, further comprising a caching subsystem configured to store responses to repetitive user inputs to avoid redundant processing.

466. The system of claim 461, further comprising a context management subsystem configured to maintain contextual information across multiple sequential user inputs from a single user.

467. The system of claim 466, wherein the context management subsystem is configured to store contextual information that exceeds context window limitations of individual artificial intelligence models.

468. The system of claim 461, wherein the routing subsystem is configured to consider cost constraints when identifying the one or more artificial intelligence models.

469. The system of claim 461, wherein the routing subsystem is configured to send the user input to multiple artificial intelligence models in parallel and select a best output based on evaluation criteria.

470. The system of claim 461, wherein the feedback subsystem is configured to use automated consensus ranking to determine quality scores for outputs.

471. A method for intelligent model selection associated with artificial intelligence model unification, the method comprising: providing, by a computing platform, access to a plurality of artificial intelligence models; maintaining, by a performance tracking subsystem, historical performance data for the plurality of artificial intelligence models across different query types; analyzing, by a routing subsystem, characteristics of a user input; identifying, by the routing subsystem, one or more artificial intelligence models from the plurality of artificial intelligence models that are predicted to perform optimally for the user input based on the historical performance data and the characteristics of the user input; directing, by the routing subsystem, the user input to the identified one or more artificial intelligence models; andAtty Dkt: 11016-8049PCT updating, by a feedback subsystem, the historical performance data based on quality of outputs generated in response to the user input.

472. The method of claim 471, wherein the characteristics of the user input comprise at least one of: subject matter, temporal context, geographic context, or complexity level.

473. The method of claim 471, wherein identifying one or more artificial intelligence models includes selecting different artificial intelligence models for different users based on user-specific preferences or permissions.

474. The method of claim 471, further comprising dynamically switching between artificial intelligence models during processing of a single user input based on intermediate results.

475. The method of claim 471, further comprising storing responses to repetitive user inputs to avoid redundant processing.

476. The method of claim 471, further comprising maintaining contextual information across multiple sequential user inputs from a single user.

477. The method of claim 476, wherein maintaining contextual information includes storing contextual information that exceeds context window limitations of individual artificial intelligence models.

478. The method of claim 471, wherein identifying one or more artificial intelligence models includes considering cost constraints.

479. The method of claim 471, further comprising sending the user input to multiple artificial intelligence models in parallel and selecting a best output based on evaluation criteria.

480. The method of claim 471, wherein updating the historical performance data includes using automated consensus ranking to determine quality scores for outputs.Extract, Iterate, Check Operations481. A system for multi-model processing with quality control associated with artificial intelligence model unification, the system comprising: a computing platform configmed to provide access to a plurality of artificial intelligence models; a model selection subsystem configured to extract a subset of artificial intelligence models from the plurality of artificial intelligence models based on selection criteria; an iteration subsystem configured to send a user input to each artificial intelligence model in the subset to generate multiple outputs; a validation subsystem configured to determine whether to apply automated verification to the multiple outputs based on configmation settings; and a ranking subsystem configured to determine whether to apply consensus ranking to the multiple outputs based on the configuration settings.

482. The system of claim 481, wherein the selection criteria comprise historical performance rankings for different query types.

483. The system of claim 481, wherein the selection criteria comprise cost tier settings that limit selection to artificial intelligence models within a specified cost range.

484. The system of claim 481, wherein the validation subsystem is configured to generate verification inputs for each output when automated verification is enabled.

485. The system of claim 484, wherein the validation subsystem is configured to iteratively improve outputs based on responses to the verification inputs.Atty Dkt: 11016-8049PCT486. The system of claim 481, wherein the ranking subsystem is configmed to send each output to each artificial intelligence model in the subset with instructions to rank all outputs when consensus ranking is enabled.

487. The system of claim 486, wherein the ranking subsystem is configmed to calculate weighted scores based on rankings from all artificial intelligence models in the subset.

488. The system of claim 487, wherein the ranking subsystem is configmed to select an output with a highest weighted score as a final output.

489. The system of claim 481, wherein the configuration settings are determined dynamically based on at least one of: complexity of the user input, available budget, or user preferences.

490. The system of claim 481, wherein the iteration subsystem is configured to process artificial intelligence models in the subset sequentially or in parallel based on system configuration.

491. A method for multi-model processing with quality control associated with artificial intelligence model unification, the method comprising: providing, by a computing platform, access to a plurality of artificial intelligence models; extracting, by a model selection subsystem, a subset of artificial intelligence models from the plurality of artificial intelligence models based on selection criteria; sending, by an iteration subsystem, a user input to each artificial intelligence model in the subset to generate multiple outputs; determining, by a validation subsystem, whether to apply automated verification to the multiple outputs based on configuration settings; and determining, by a ranking subsystem, whether to apply consensus ranking to the multiple outputs based on the configuration settings.

492. The method of claim 491, wherein the selection criteria comprise historical performance rankings for different query types.

493. The method of claim 491, wherein the selection criteria comprise cost tier settings that limit selection to artificial intelligence models within a specified cost range.

494. The method of claim 491, further comprising generating verification inputs for each output when automated verification is enabled.

495. The method of claim 494, further comprising iteratively improving outputs based on responses to the verification inputs.

496. The method of claim 491, further comprising sending each output to each artificial intelligence model in the subset with instructions to rank all outputs when consensus ranking is enabled.

497. The method of claim 496, further comprising calculating weighted scores based on rankings from all artificial intelligence models in the subset.

498. The method of claim 497, further comprising selecting an output with a highest weighted score as a final output.

499. The method of claim 491, wherein the configuration settings are determined dynamically based on at least one of: complexity of the user input, available budget, or user preferences.

500. The method of claim 491, wherein sending the user input to each artificial intelligence model includes processing artificial intelligence models in the subset sequentially or in parallel based on system configuration.Atty Dkt: 11016-8049PCTResponse Model Consensus Ranking (RMCR)501. A system for consensus based output evaluation associated with artificial intelligence model unification, the system comprising: a computing platform configured to provide access to a plurality of artificial intelligence models; a response generation subsystem configured to generate multiple candidate outputs by providing a user input to multiple artificial intelligence models from the plurality of artificial intelligence models; an evaluation subsystem configured to: send each candidate output to each of the multiple artificial intelligence models along with instructions to evaluate all candidate outputs; receive evaluation data from each of the multiple artificial intelligence models, wherein the evaluation data comprises rankings or scores for each candidate output; and a selection subsystem configmed to: calculate aggregate evaluation scores for each candidate output based on the evaluation data from all of the multiple artificial intelligence models; and select a final output from the candidate outputs based on the aggregate evaluation scores.

502. The system of claim 501, wherein the instructions to evaluate specify evaluation criteria comprising at least two of: quality, accuracy, clarity, or conciseness.

503. The system of claim 501, wherein the evaluation data comprises both a ranking and a numerical score for each candidate output.

504. The system of claim 503, wherein the numerical score ranges from one to one hundred.

505. The system of claim 501, wherein the evaluation subsystem is further configured to request confidence levels from each artificial intelligence model regarding their evaluations.

506. The system of claim 505, wherein the selection subsystem is configmed to weight evaluation data based on the confidence levels when calculating aggregate evaluation scores.

507. The system of claim 501, wherein the selection subsystem is configmed to consider latency information when calculating aggregate evaluation scores.

508. The system of claim 507, wherein lower latency outputs receive higher aggregate evaluation scores when other evaluation factors are equal.

509. The system of claim 501, further comprising a learning subsystem configmed to store winning output information along with characteristics of the user input to improve future model selection.

510. The system of claim 501, wherein the evaluation subsystem is configured to send evaluation requests to the multiple artificial intelligence models in parallel.

511. A method for consensus based output evaluation associated with artificial intelligence model unification, the method comprising: providing, by a computing platform, access to a plurality of artificial intelligence models; generating, by a response generation subsystem, multiple candidate outputs by providing a user input to multiple artificial intelligence models from the plurality of artificial intelligence models; sending, by an evaluation subsystem, each candidate output to each of the multiple artificial intelligence models along with instructions to evaluate all candidate outputs;Atty Dkt: 11016-8049PCT receiving, by the evaluation subsystem, evaluation data from each of the multiple artificial intelligence models, wherein the evaluation data comprises rankings or scores for each candidate output; calculating, by a selection subsystem, aggregate evaluation scores for each candidate output based on the evaluation data from all of the multiple artificial intelligence models; and selecting, by the selection subsystem, a final output from the candidate outputs based on the aggregate evaluation scores.

512. The method of claim 511, wherein the instructions to evaluate specify evaluation criteria comprising at least two of: quality, accuracy, clarity, or conciseness.

513. The method of claim 511, wherein the evaluation data comprises both a ranking and a numerical score for each candidate output.

514. The method of claim 513, wherein the numerical score ranges from one to one hundred.

515. The method of claim 511, further comprising requesting confidence levels from each artificial intelligence model regarding their evaluations.

516. The method of claim 515, wherein calculating aggregate evaluation scores includes weighting evaluation data based on the confidence levels.

517. The method of claim 511, wherein calculating aggregate evaluation scores includes considering latency information.

518. The method of claim 517, wherein lower latency outputs receive higher aggregate evaluation scores when other evaluation factors are equal.

519. The method of claim 511, further comprising storing winning output information along with characteristics of the user input to improve future model selection.

520. The method of claim 511, wherein sending evaluation requests includes sending evaluation requests to the multiple artificial intelligence models in parallel.Prompt Model Consensus Ranking (PMCR)521. A system for consensus based input decomposition evaluation associated with artificial intelligence model unification, the system comprising: a computing platform configmed to provide access to a plurality of artificial intelligence models; a decomposition generation subsystem configured to generate multiple candidate decompositions of a user input by providing the user input to multiple artificial intelligence models from the plurality of artificial intelligence models, wherein each candidate decomposition comprises a plurality of component inputs; an evaluation subsystem configured to: send each candidate decomposition to each of the multiple artificial intelligence models along with instructions to evaluate all candidate decompositions; receive evaluation data from each of the multiple artificial intelligence models, wherein the evaluation data comprises rankings or scores for each candidate decomposition; and a selection subsystem configmed to: calculate aggregate evaluation scores for each candidate decomposition based on the evaluation data from all of the multiple artificial intelligence models; and select a final decomposition from the candidate decompositions based on the aggregate evaluation scores for use in processing the user input.Atty Dkt: 11016-8049PCT522. The system of claim 521, wherein the instructions to evaluate specify criteria for assessing quality of decompositions including at least one of: comprehensiveness, proper sequencing, or absence of redundancy.

523. The system of claim 521, wherein each candidate decomposition includes both the plurality of component inputs and a proposed processing sequence for the component inputs.

524. The system of claim 521, further comprising a validation subsystem configured to generate verification inputs to challenge aspects of the candidate decompositions before evaluation.

525. The system of claim 524, wherein the verification inputs include questions about whether component inputs are in proper sequence and whether the decomposition is comprehensive.

526. The system of claim 521, wherein the evaluation subsystem is configured to request confidence levels from each artificial intelligence model regarding their evaluations of the candidate decompositions.

527. The system of claim 521, further comprising a processing subsystem configured to process the plurality of component inputs from the selected final decomposition using one or more artificial intelligence models.

528. The system of claim 527, wherein the processing subsystem is configured to use a proposed processing sequence from the final decomposition to determine an order for processing the plurality of component inputs.

529. The system of claim 521, further comprising a learning subsystem configured to store information about the selected final decomposition to improve a model selection algorithm for future input decomposition tasks.

530. The system of claim 521, wherein the decomposition generation subsystem is configured to generate the multiple candidate decompositions in parallel.

531. A method for consensus based input decomposition evaluation associated with artificial intelligence model unification, the method comprising: providing, by a computing platform, access to a plurality of artificial intelligence models; generating, by a decomposition generation subsystem, multiple candidate decompositions of a user input by providing the user input to multiple artificial intelligence models from the plurality of artificial intelligence models, wherein each candidate decomposition comprises a plurality of component inputs; sending, by an evaluation subsystem, each candidate decomposition to each of the multiple artificial intelligence models along with instructions to evaluate all candidate decompositions; receiving, by the evaluation subsystem, evaluation data from each of the multiple artificial intelligence models, wherein the evaluation data comprises rankings or scores for each candidate decomposition; calculating, by a selection subsystem, aggregate evaluation scores for each candidate decomposition based on the evaluation data from all of the multiple artificial intelligence models; and selecting, by the selection subsystem, a final decomposition from the candidate decompositions based on the aggregate evaluation scores for use in processing the user input.

532. The method of claim 531, wherein the instructions to evaluate specify criteria for assessing quality of decompositions including at least one of: comprehensiveness, proper sequencing, or absence of redundancy.

533. The method of claim 531, wherein each candidate decomposition includes both the plurality of component inputs and a proposed processing sequence for the component inputs.

534. The method of claim 531, further comprising generating verification inputs to challenge aspects of the candidate decompositions before evaluation.

535. The method of claim 534, wherein the verification inputs include questions about whether component inputs are in proper sequence and whether the decomposition is comprehensive.Atty Dkt: 11016-8049PCT536. The method of claim 531, further comprising requesting confidence levels from each artificial intelligence model regarding their evaluations of the candidate decompositions.

537. The method of claim 531, further comprising processing the plurality of component inputs from the selected final decomposition using one or more artificial intelligence models.

538. The method of claim 537, further comprising using a proposed processing sequence from the final decomposition to determine an order for processing the plurality of component inputs.

539. The method of claim 531, further comprising storing information about the selected final decomposition to improve a model selection algorithm for future input decomposition tasks.

540. The method of claim 531, wherein generating the multiple candidate decompositions includes generating the multiple candidate decompositions in parallel.Confidence Level and Latency541. A system for quality -weighted model evaluation associated with artificial intelligence model unification, the system comprising: a computing platform configured to provide access to a plurality of artificial intelligence models; a response generation subsystem configured to generate multiple candidate outputs by providing a user input to multiple artificial intelligence models from the plurality of artificial intelligence models; an evaluation subsystem configured to: send each candidate output to each of the multiple artificial intelligence models along with instructions to evaluate all candidate outputs and provide certainty measures indicating confidence in their evaluations; receive evaluation data from each of the multiple artificial intelligence models, wherein the evaluation data comprises scores for each candidate output and certainty measures for each evaluation; a performance measurement subsystem configured to determine temporal performance characteristics for generating each candidate output; and a selection subsystem configmed to: calculate weighted evaluation scores for each candidate output based on the evaluation data, the certainty measures, and the temporal performance characteristics; and select a final output from the candidate outputs based on the weighted evaluation scores.

542. The system of claim 541, wherein the certainty measures comprise confidence scores on a numerical scale.

543. The system of claim 541, wherein the selection subsystem is configured to assign greater weight to evaluation data associated with higher certainty measures.

544. The system of claim 541, wherein the temporal performance characteristics comprise response times for generating each candidate output.

545. The system of claim 544, wherein the selection subsystem is configured to favor candidate outputs with lower response times when quality scores are similar.

546. The system of claim 541, wherein the temporal performance characteristics comprise latency measurements.

547. The system of claim 541, further comprising a storage subsystem configured to store the certainty measures along with the evaluation data for use in improving future model selection.

548. The system of claim 541, wherein the selection subsystem is configured to apply configurable weighting factors to balance consideration of quality scores, certainty measures, and temporal performance characteristics.Atty Dkt: 11016-8049PCT549. The system of claim 548, wherein the configurable weighting factors are adjustable based on user preferences.

550. The system of claim 541, wherein the evaluation subsystem is configured to request that each artificial intelligence model provide reasoning for their certainty measures.

551. A method for quality -weighted model evaluation associated with artificial intelligence model unification, the method comprising: providing, by a computing platform, access to a plurality of artificial intelligence models; generating, by a response generation subsystem, multiple candidate outputs by providing a user input to multiple artificial intelligence models from the plurality of artificial intelligence models; sending, by an evaluation subsystem, each candidate output to each of the multiple artificial intelligence models along with instructions to evaluate all candidate outputs and provide certainty measures indicating confidence in their evaluations; receiving, by the evaluation subsystem, evaluation data from each of the multiple artificial intelligence models, wherein the evaluation data comprises scores for each candidate output and certainty measures for each evaluation; determining, by a performance measurement subsystem, temporal performance characteristics for generating each candidate output; calculating, by a selection subsystem, weighted evaluation scores for each candidate output based on the evaluation data, the certainty measures, and the temporal performance characteristics; and selecting, by the selection subsystem, a final output from the candidate outputs based on the weighted evaluation scores.

552. The method of claim 551, wherein the certainty measures comprise confidence scores on a numerical scale.

553. The method of claim 551, wherein calculating weighted evaluation scores includes assigning greater weight to evaluation data associated with higher certainty measures.

554. The method of claim 551, wherein the temporal performance characteristics comprise response times for generating each candidate output.

555. The method of claim 554, wherein calculating weighted evaluation scores includes favoring candidate outputs with lower response times when quality scores are similar.

556. The method of claim 551, wherein the temporal performance characteristics comprise latency measurements.

557. The method of claim 551, further comprising storing the certainty measures along with the evaluation data for use in improving future model selection.

558. The method of claim 551, wherein calculating weighted evaluation scores includes applying configurable weighting factors to balance consideration of quality scores, certainty measures, and temporal performance characteristics.

559. The method of claim 558, wherein the configurable weighting factors are adjustable based on user preferences.

560. The method of claim 551, further comprising requesting that each artificial intelligence model provide reasoning for their certainty measures.GOVERNANCE AND AUTHORIZATION PROCESSES561. A system for managing access control and policy enforcement associated with artificial intelligence model unification, the system comprising: a computing platform configmed to provide access to a plurality of artificial intelligence models;Atty Dkt: 11016-8049PCT a policy management subsystem configured to define and store organizational controls for use of the plurality of artificial intelligence models; an access control subsystem configmed to: assign access rights to users based on organizational positions; and enforce the access rights by permitting or denying user requests to access artificial intelligence models based on the organizational positions; an authorization subsystem configured to determine resource allocation for users based on user classifications and the organizational controls; and an audit subsystem configmed to capture and store information related to user activities and system events.

562. The system of claim 561, wherein the organizational controls comprise mles specifying which users can access which artificial intelligence models.

563. The system of claim 561, wherein the access control subsystem implements role-based access control wherein access rights are assigned based on user roles within an organization.

564. The system of claim 561, wherein the authorization subsystem is configured to assign priority levels to users that determine resource allocation when system capacity is limited.

565. The system of claim 564, wherein higher priority users receive preferential access to artificial intelligence models and faster response times.

566. The system of claim 561, further comprising an authentication subsystem configured to verify user identity using multiple verification factors.

567. The system of claim 566, wherein the multiple verification factors comprise at least two of: passwords, biometric data, security tokens, or one-time codes.

568. The system of claim 561, further comprising an encryption subsystem configured to apply cryptographic transformation to user inputs and model outputs.

569. The system of claim 568, wherein cryptographic keys used for the cryptographic transformation are maintained exclusively by clients and not accessible to the computing platform.

570. The system of claim 561, wherein the audit subsystem is configured to create logs that include timestamps, user identifiers, actions performed, and artificial intelligence models accessed.

571. A method for managing access control and policy enforcement associated with artificial intelligence model unification, the method comprising: providing, by a computing platform, access to a plurality of artificial intelligence models; defining and storing, by a policy management subsystem, organizational controls for use of the plurality of artificial intelligence models; assigning, by an access control subsystem, access rights to users based on organizational positions; enforcing, by the access control subsystem, the access rights by permitting or denying user requests to access artificial intelligence models based on the organizational positions; determining, by an authorization subsystem, resource allocation for users based on user classifications and the organizational controls; and capturing and storing, by an audit subsystem, information related to user activities and system events.

572. The method of claim 571, wherein the organizational controls comprise rules specifying which users can access which artificial intelligence models.Atty Dkt: 11016-8049PCT573. The method of claim 571, wherein assigning access rights comprises implementing role-based access control wherein access rights are assigned based on user roles within an organization.

574. The method of claim 571, wherein determining resource allocation includes assigning priority levels to users that determine resource allocation when system capacity is limited.

575. The method of claim 574, wherein higher priority users receive preferential access to artificial intelligence models and faster response times.

576. The method of claim 571, further comprising verifying user identity using multiple verification factors.

577. The method of claim 576, wherein the multiple verification factors comprise at least two of: passwords, biometric data, security tokens, or one-time codes.

578. The method of claim 571, further comprising applying cryptographic transformation to user inputs and model outputs.

579. The method of claim 578, wherein cryptographic keys used for the cryptographic transformation are maintained exclusively by clients and not accessible to the computing platform.

580. The method of claim 571, wherein capturing and storing information includes creating logs that include timestamps, user identifiers, actions performed, and artificial intelligence models accessed.Automated compliance management581. A system for automated compliance management associated with artificial intelligence model unification, the system comprising: a computing platform configured to provide access to a plurality of artificial intelligence models; a compliance subsystem configured to: store regulatory requirements applicable to processing of user data; monitor operations of the plurality of artificial intelligence models to detect potential regulatory violations; and automatically enforce compliance controls to ensure conformance with the regulatory requirements; a user verification subsystem configured to: collect features relating to users; calculate trust scores for users based on the features; and adjust access permissions based on the trust scores; and a transaction security subsystem configured to apply protective measures to transactions involving the plurality of artificial intelligence models.

582. The system of claim 581, wherein the regulatory requirements comprise data privacy regulations including at least one of GDPR, CCPA, or HIPAA requirements.

583. The system of claim 581, wherein the compliance subsystem is configured to automatically block operations that would violate the regulatory requirements.

584. The system of claim 581, wherein the features relating to users comprise at least one of: historical usage patterns, authentication history, geographic location, or device information.

585. The system of claim 581, wherein users with lower tmst scores are subject to more restrictive access controls than users with higher trust scores.Atty Dkt: 11016-8049PCT586. The system of claim 581, wherein the protective measures comprise encryption, access logging, and anomaly detection.

587. The system of claim 581, further comprising a state monitoring subsystem configured to track status information for entities involved in transactions.

588. The system of claim 587, wherein the state monitoring subsystem is configured to detect suspicious state changes that may indicate security threats.

589. The system of claim 581, wherein the compliance subsystem is configured to generate compliance reports documenting adherence to the regulatory requirements.

590. The system of claim 581, wherein the compliance subsystem is configured to apply different regulatory requirements based on geographic location of users or location where data is processed.

591. A method for automated compliance management associated with artificial intelligence model unification, the method comprising: providing, by a computing platform, access to a plurality of artificial intelligence models; storing, by a compliance subsystem, regulatory requirements applicable to processing of user data; monitoring, by the compliance subsystem, operations of the plurality of artificial intelligence models to detect potential regulatory violations; automatically enforcing, by the compliance subsystem, compliance controls to ensure conformance with the regulatory requirements; collecting, by a user verification subsystem, features relating to users; calculating, by the user verification subsystem, trust scores for users based on the features; adjusting, by the user verification subsystem, access permissions based on the trust scores; and applying, by a transaction security subsystem, protective measures to transactions involving the plurality of artificial intelligence models.

592. The method of claim 591, wherein the regulatory requirements comprise data privacy regulations including at least one of GDPR, CCPA, or HIPAA requirements.

593. The method of claim 591, wherein automatically enforcing compliance controls includes automatically blocking operations that would violate the regulatory requirements.

594. The method of claim 591, wherein the features relating to users comprise at least one of: historical usage patterns, authentication history, geographic location, or device information.

595. The method of claim 591, wherein users with lower trust scores are subject to more restrictive access controls than users with higher trust scores.

596. The method of claim 591, wherein the protective measures comprise encryption, access logging, and anomaly detection.

597. The method of claim 591, further comprising tracking status information for entities involved in transactions.

598. The method of claim 597, further comprising detecting suspicious state changes that may indicate security threats.

599. The method of claim 591, further comprising generating compliance reports documenting adherence to the regulatory requirements.

600. The method of claim 591, wherein storing regulatory requirements includes applying different regulatory requirements based on geographic location of users or location where data is processed.Atty Dkt: 11016-8049PCTKNOWLEDGE GROUNDING AND INTEGRATION601. A system for artificial intelligence model unification and access, the system comprising: a knowledge representation system configured to store structured information; an information retrieval system configured to retrieve data from the knowledge representation system based on user inputs; a data storage system configured to store embedded data; a processing system configured to: generate a knowledge representation dynamically based on available data without requiring manual schema setup; perform hybrid semantic and lexical processing on the user inputs; combine data from private sources and public sources for grounding processing of the user inputs; and generate outputs based on the retrieved data and the user inputs.

602. The system of claim 601, wherein the processing system is further configured to create the knowledge representation dynamically using at least one computational model.

603. The system of claim 602, wherein the at least one computational model creates the knowledge representation using proprietary data provided by a user.

604. The system of claim 601, wherein the processing system is further configured to generate the knowledge representation on-the-fly in response to receiving the user inputs.

605. The system of claim 601, wherein the hybrid semantic and lexical processing comprises: performing semantic vectorization on a fixed amount of information; and determining distances between topics in the user inputs and stored data.

606. The system of claim 601, wherein the processing system is further configured to stitch together multiple data storage systems to appear as a unified data storage system.

607. The system of claim 606, wherein stitching together the multiple data storage systems comprises creating a knowledge representation of multiple knowledge representations.

608. The system of claim 601, wherein the processing system is further configured to: create custom agents to decompose the user inputs into subtopics; and parallelize processing of the subtopics using the information retrieval system.

609. The system of claim 601, wherein the processing system is further configured to select an optimal set of information to include in a context with the user inputs.

610. The system of claim 601, further comprising a context management system configmed to: maintain a transient cache of information between a context window of a computational model and the data storage system; and automatically reload pertinent information into the context window when the context window is exceeded.

611. A method for grounding artificial intelligence processing, the method comprising: receiving, by one or more data processors, a user input; dynamically generating, by the one or more data processors, a knowledge representation based on available data without requiring pre-processing or manual schema setup;Atty Dkt: 11016-8049PCT performing, by the one or more data processors, hybrid processing comprising semantic processing and lexical processing on the user input; retrieving, by the one or more data processors, information from a data storage system based on the knowledge representation and the user input; combining, by the one or more data processors, data from private sources and public sources for grounding processing of the user input; and generating, by the one or more data processors, an output based on the retrieved information and the user input.

612. The method of claim 611, wherein dynamically generating the knowledge representation comprises using at least one computational model to create relationships across different topics and keywords.

613. The method of claim 611, wherein performing hybrid processing comprises: performing semantic vectorization to create embeddings of data; and performing lexical analysis to identify keywords and relationships.

614. The method of claim 611, further comprising: decomposing the user input into multiple subtopics using custom agents; and processing the multiple subtopics in parallel.

615. The method of claim 611, further comprising stitching together multiple pre-processed data storage systems based on context of the user input.

616. The method of claim 611, further comprising selecting which knowledge representations to use based on context of the user input.

617. The method of claim 611, wherein combining data from private sources and public sources comprises automatically managing and selecting private datasets without requiring explicit user configuration.

618. The method of claim 611, further comprising: maintaining a transient cache of information between a context window of a computational model and static data; and evaluating pertinent information to reload into the context window when the context window is exceeded.

619. The method of claim 611, further comprising performing custom embedding and training for scenarios where standard processing falls short.

620. The method of claim 611, wherein dynamically generating the knowledge representation comprises: prompting a computational model to create a new knowledge representation with seed information; and grounding available information based on the user input.MULTILINGUAL AND MULTI-MODAL SUPPORT621. A system for multi-modal and multilingual artificial intelligence processing, the system comprising: a language processing system configured to handle user inputs and generate outputs in multiple languages; a multi-format processing system configured to process user inputs comprising multiple data types including text, images, video, and audio; a content generation system configured to generate outputs in multiple formats; and a processing system configured to: identify data types present in the user inputs; decompose the user inputs into components based on the identified data types;Atty Dkt: 11016-8049PCT route each component to a computational model suited for processing that component; and generate outputs based on processing results from the computational models.

622. The system of claim 621, wherein the multi-format processing system is configured to process user inputs comprising at least two of: text, still images, video, audio, and sensor data.

623. The system of claim 621, wherein the processing system is further configured to select which of multiple computational models to use based on temporal context, geographic location, or content context associated with the user inputs.

624. The system of claim 621, wherein the processing system is further configured to route different components of a single user input to different computational models based on capabilities of the computational models.

625. The system of claim 621, wherein the content generation system is configured to generate outputs comprising at least two of: text, images, charts, audio, and video.

626. The system of claim 621, further comprising an interface system configmed to display outputs from multiple computational models in a unified interface.

627. The system of claim 626, wherein the unified interface comprises multiple windows, each displaying results from computational models with different capabilities.

628. The system of claim 621, wherein the processing system is further configured to identify which of multiple computational models covering same capabilities are superior based on at least one of: temporal nature, context, and geographic location.

629. The system of claim 621, wherein the language processing system is configmed to process user inputs in a first language and generate outputs in a second language different from the first language.

630. The system of claim 621, wherein the processing system is further configured to adapt content of the user inputs based on identified data types before routing to the computational models.

631. A method for multi-modal artificial intelligence processing, the method comprising: receiving, by one or more data processors, a user input comprising multiple data types; identifying, by the one or more data processors, the multiple data types present in the user input; decomposing, by the one or more data processors, the user input into multiple components based on the identified data types; routing, by the one or more data processors, each component to a computational model suited for processing that component; processing, by the one or more data processors, each component using the routed computational model; and generating, by the one or more data processors, an output based on processing results from the computational models.

632. The method of claim 631, wherein identifying the multiple data types comprises identifying at least two of: text, images, video, audio, and sensor data.

633. The method of claim 631, wherein routing each component comprises selecting computational models based on temporal context, geographic location, or content context associated with the user input.

634. The method of claim 631, further comprising generating the output in multiple formats comprising at least two of: text, images, charts, audio, and video.Atty Dkt: 11016-8049PCT635. The method of claim 631, further comprising displaying the output in a unified interface that seamlessly integrates results from multiple computational models.

636. The method of claim 631, wherein the user input comprises content in a first language, and wherein generating the output comprises generating content in a second language different from the first language.

637. The method of claim 631, further comprising selecting which of multiple computational models handling same data types to use based on performance metrics.

638. The method of claim 631, wherein decomposing the user input comprises identifying which computational models are better at processing different aspects of the user input.

639. The method of claim 631, further comprising combining data from multiple knowledge representation systems based on the identified data types and context of the user input.

640. The method of claim 631, further comprising adapting content of the user input for each component based on capabilities of the computational model to which that component is routed.USER INTERACTION AND BEHAVIOR MONITORING641. A system for monitoring and managing user interactions with artificial intelligence models, the system comprising: a tracking system configmed to track user behavior and engagement metrics for users interacting with computational models; a monitoring system configured to monitor outcomes associated with the user interactions; a user analysis system configmed to generate user representations based on tracked behavior; a targeting system configured to select users or user groups for engagement; and a processing system configured to: generate engagement metrics based on the tracked user behavior; create user representations that model individual users; autonomously initiate engagement with users based on the engagement metrics; and adapt computational model selection based on the monitored outcomes.

642. The system of claim 641, wherein the user analysis system is configmed to generate digital representations of users that simulate user characteristics and preferences.

643. The system of claim 641, further comprising a gamification system configured to apply game mechanics to user interfaces to enhance user engagement.

644. The system of claim 643, wherein the gamification system is configured to provide rewards, achievements, or incentives based on user interactions.

645. The system of claim 641, wherein the processing system is further configured to track user states and flows through interaction sequences.

646. The system of claim 641, further comprising a dialog system configured to: identify potentially incorrect assumptions in user inputs; generate clarifying questions for users; and incorporate user responses into context for computational model processing.

647. The system of claim 646, wherein the dialog system is further configured to: state inferred user intent; and request user confirmation of the inferred user intent.Atty Dkt: 11016-8049PCT648. The system of claim 646, wherein the dialog system is further configured to identify ambiguities in user inputs that can be interpreted in multiple ways.

649. The system of claim 641, wherein the monitoring system is configmed to track outcomes comprising at least one of: accuracy metrics, user satisfaction metrics, task completion metrics, and performance metrics.

650. The system of claim 641, wherein the targeting system is configured to select users based on user representations and engagement metrics.

651. A method for managing user interactions with artificial intelligence systems, the method comprising: tracking, by one or more data processors, user behavior for users interacting with computational models; determining, by the one or more data processors, engagement metrics based on the tracked user behavior; generating, by the one or more data processors, user representations that model individual users based on the tracked user behavior; monitoring, by the one or more data processors, outcomes associated with user interactions with the computational models; autonomously initiating, by the one or more data processors, engagement with users based on the engagement metrics; and adapting, by the one or more data processors, computational model selection based on the monitored outcomes.

652. The method of claim 651, wherein generating user representations comprises creating digital twins that represent users and simulate user characteristics.

653. The method of claim 651, further comprising applying gamification elements to user interfaces to enhance user engagement.

654. The method of claim 651, further comprising: identifying potentially incorrect assumptions in user inputs; generating clarifying questions for users; receiving user responses to the clarifying questions; and incorporating the user responses into context for computational model processing.

655. The method of claim 654, wherein identifying potentially incorrect assumptions comprises analyzing ambiguities in user inputs that can be interpreted in multiple ways.

656. The method of claim 651, further comprising: inferring user intent from user inputs; presenting the inferred user intent to users; and requesting user confirmation of the inferred user intent.

657. The method of claim 651, wherein monitoring outcomes comprises tracking at least one of: accuracy metrics, user satisfaction metrics, task completion metrics, and performance metrics.

658. The method of claim 651, further comprising selecting users or user groups for targeted engagement based on the user representations and engagement metrics.

659. The method of claim 651, further comprising tracking user states and flows through interaction sequences to identify patterns.

660. The method of claim 651, wherein autonomously initiating engagement comprises automatically communicating with users without explicit user requests.Atty Dkt: 11016-8049PCTPHYSICAL ENTITIES AND HARDWARE661. A system for artificial intelligence model unification and access, the system comprising: a set of computing devices configured to execute artificial intelligence processing; a processing hardware component comprising at least one of: a central processing unit, a graphics processing unit, a neural network processor, and an artificial intelligence system-on-chip; a data storage device configured to store instructions and data; a network infrastructure configured to facilitate communication between the computing devices; and a processing system configured to: receive user inputs from the computing devices; route the user inputs to computational models based on model selection algorithms; process the user inputs using the computational models executed on the processing hardware component; and transmit outputs to the computing devices via the network infrastructure.

662. The system of claim 661, wherein the processing hardware component comprises an artificial intelligence system-on-chip that integrates multiple processing functions on a single integrated circuit.

663. The system of claim 662, wherein the artificial intelligence system-on-chip comprises specialized circuitry for neural network processing.

664. The system of claim 661, wherein the set of computing devices comprises at least one of: mobile devices, tablets, personal computers, laptops, and servers.

665. The system of claim 661, wherein the network infrastructure comprises a cloud computing system.

666. The system of claim 665, wherein the cloud computing system comprises at least one of: private cloud infrastructure, community cloud infrastructure, and hybrid cloud infrastructure.

667. The system of claim 661, further comprising a sensor system configured to: capture data from an environment using sensors; and provide the captured data as input to the computational models.

668. The system of claim 667, wherein the sensors comprise at least one of: image sensors, video sensors, temperature sensors, pressure sensors, and motion sensors.

669. The system of claim 661, wherein the processing hardware component comprises a converged artificial intelligence chipset that integrates multiple artificial intelligence processing functions.

670. The system of claim 661, further comprising an edge computing system configured to perform artificial intelligence processing at an edge location closer to data sources.

671. A method for distributed artificial intelligence processing, the method comprising: receiving, by computing devices, user inputs; transmitting, by the computing devices, the user inputs via a network infrastructure to a processing system; selecting, by one or more data processors, computational models to process the user inputs based on model selection algorithms; executing, by processing hardware comprising at least one of a central processing unit, a graphics processing unit, a neural network processor, and an artificial intelligence system-on-chip, the selected computational models to process the user inputs;Atty Dkt: 11016-8049PCT generating, by the one or more data processors, outputs based on processing results from the computational models; and transmitting, by the one or more data processors, the outputs to the computing devices via the network infrastructure.

672. The method of claim 671, wherein executing the selected computational models comprises using an artificial intelligence system-on-chip that integrates multiple processing functions on a single integrated circuit.

673. The method of claim 671, wherein the network infrastructure comprises a cloud computing system, and wherein executing the selected computational models comprises processing the user inputs on cloud-based resources.

674. The method of claim 671, further comprising: capturing data from an environment using sensors integrated with the computing devices; and providing the captured data as input to the computational models.

675. The method of claim 674, wherein capturing data comprises capturing at least one of: images, video, temperature data, pressure data, and motion data.

676. The method of claim 671, further comprising performing sensor fusion to combine data from multiple sensors before providing the combined data to the computational models.

677. The method of claim 671, wherein selecting computational models comprises distributing processing between cloud-based resources and edge computing systems.

678. The method of claim 671, wherein the processing hardware comprises a converged artificial intelligence chipset that integrates multiple artificial intelligence processing functions.

679. The method of claim 671, further comprising optimizing allocation of processing across multiple processing hardware components based on computational requirements.

680. The method of claim 671, further comprising storing the outputs and processing history on the computing devices or in a cloud-based data storage device.FEATURES, CAPABILITIES, AND TRANSACTIONS681. A system for facilitating transactions and services in an artificial intelligence platform, the system comprising: a marketplace system configured to provide access to computational models and digital assets; a transaction system configured to process transactions using at least one of: digital payment means, cashless transaction processing, and cryptocurrency; a security system configured to secure transactions and data; a compliance system configured to ensure compliance with regulatory requirements; a resource management system configured to provision and optimize computing resources; and a processing system configured to: enable users to access computational models via the marketplace system; process user transactions using the transaction system; apply security measures to protect user data and transactions; monitor compliance with legal and regulatory requirements; and optimize allocation of computing resources based on usage patterns.

682. The system of claim 681, wherein the marketplace system comprises an embedded marketplace for digital twins that represent users, devices, or systems.Atty Dkt: 11016-8049PCT683. The system of claim 681, wherein the marketplace system comprises an embedded marketplace for computational models that allows users to access multiple models.

684. The system of claim 681, wherein the transaction system is configured to process transactions using digital wallets that store value for users.

685. The system of claim 681, wherein the transaction system is configured to process cryptocurrency transactions using blockchain technology.

686. The system of claim 681, wherein the security system is configured to apply encryption to user inputs and outputs, with encryption keys maintained exclusively by users.

687. The system of claim 681, wherein the security system is further configured to implement at least one of: multifactor authentication, role-based access control, and audit logging.

688. The system of claim 681, wherein the compliance system is configured to ensure compliance with data privacy regulations comprising at least one of: HIPAA, GDPR, and regional data protection laws.

689. The system of claim 681, wherein the resource management system is configured to optimize at least one of: computational resources, network resources, and energy resources.

690. The system of claim 681, further comprising a reporting system configured to generate reports on usage, configmation, and billing for users.

691. The system of claim 681, further comprising a competitive evaluation system configmed to: enable multiple computational models to compete in processing user inputs; and generate metrics on model performance for model developers.

692. A method for managing transactions and services in an artificial intelligence platform, the method comprising: providing, by one or more data processors, access to computational models via a marketplace system; receiving, by the one or more data processors, a transaction request from a user; processing, by the one or more data processors, the transaction using at least one of: digital payment means, cashless transaction processing, and cryptocurrency; applying, by the one or more data processors, security measures to protect user data and the transaction; monitoring, by the one or more data processors, compliance with legal and regulatory requirements; provisioning, by the one or more data processors, computing resources to process user requests; and optimizing, by the one or more data processors, allocation of the computing resources based on usage patterns.

693. The method of claim 692, wherein providing access to computational models comprises providing access via an embedded marketplace that allows users to select from multiple computational models.

694. The method of claim 692, wherein processing the transaction comprises processing payment using a digital wallet associated with the user.

695. The method of claim 692, wherein processing the transaction comprises processing cryptocurrency transactions using blockchain technology.

696. The method of claim 692, wherein applying security measures comprises: encrypting user inputs and outputs using encryption keys; and maintaining the encryption keys exclusively with users.

697. The method of claim 692, wherein applying security measures further comprises implementing at least one of: multi-factor authentication, role-based access control, and audit logging.Atty Dkt: 11016-8049PCT698. The method of claim 692, wherein monitoring compliance comprises ensuring compliance with data privacy regulations comprising at least one of: HIPAA, GDPR, and regional data protection laws.

699. The method of claim 692, wherein optimizing allocation of computing resources comprises optimizing at least one of: computational resources, network resources, and energy resources.

700. The method of claim 692, further comprising: enabling multiple computational models to compete in processing user inputs; generating performance metrics for the computational models; and providing the performance metrics to model developers to facilitate model improvement.ALGORITHMS701. A routing system for artificial intelligence model unification and access, the system comprising: one or more processors; and one or more memories storing instructions that, when executed by the one or more processors, cause the system to: receive a user input via an input interface; calculate input characteristics data by extracting metadata from the user input, wherein the input characteristics data comprises at least one of: key terms, temporal context, or complexity metric; determine configuration parameters for input processing based at least in part on the input characteristics data; select a first set of processing resources from a plurality of available processing resources based at least in part on the input characteristics data and historical performance data; decompose the user input into a plurality of sub-inputs when decomposition logic indicates decomposition is appropriate; issue the user input or the plurality of sub-inputs to the first set of processing resources; receive a first set of outputs from the first set of processing resources; generate validation data comprising challenge information configmed to test accuracy of the first set of outputs; issue the validation data to the first set of processing resources to generate refined outputs; when consensus logic is enabled, obtain ranking data from the first set of processing resources, wherein each processing resource in the first set ranks the refined outputs based on quality metrics; calculate weighted scores for the refined outputs based on the ranking data; and select a final output from the refined outputs based on the weighted scores.

702. The system of claim 701, wherein the instructions further cause the system to: determine a resource allocation strategy based on budget constraint parameters and current expenditure data; and adjust the determination of configuration parameters based on the resource allocation strategy.

703. The system of claim 701, wherein calculating the input characteristics data comprises: sending a metadata extraction request to a selected processing resource from the plurality of available processing resources; receiving metadata response data from the selected processing resource; andAtty Dkt: 11016-8049PCT parsing the metadata response data to extract the key terms, the temporal context, and the complexity metric.

704. The system of claim 703, wherein the complexity metric comprises a numerical value ranging from 1 to 5 indicating a sophistication level of the user input.

705. The system of claim 701, wherein decomposing the user input comprises: sending a decomposition request to at least one processing resource in the first set of processing resources, wherein the decomposition request instructs the at least one processing resource to break down the user input into a series of interconnected sub-inputs; and receiving decomposition output data comprising the plurality of sub-inputs.

706. The system of claim 705, wherein the instructions further cause the system to: when validation logic for input processing is enabled: send validation prompts to the at least one processing resource to challenge accuracy of the decomposition output data; and update the plurality of sub-inputs based on validation responses received from the at least one processing resource.

707. The system of claim 701, wherein the instructions further cause the system to: maintain performance tracking data associating input characteristics with processing resource performance metrics; update the performance tracking data based on the ranking data and the weighted scores; and utilize the updated performance tracking data when selecting processing resources for subsequent user inputs.

708. The system of claim 701, wherein the quality metrics comprise at least two of: accuracy measure, clarity measure, conciseness measure, confidence level, or latency value.

709. The system of claim 701, wherein the instructions further cause the system to: when agentic processing logic is enabled: analyze the user input to identify decomposition opportunities; create specialized processing agents configured to handle specific sub-tasks identified in the decomposition opportunities; track frequency of agent usage; and when frequency of a particular agent exceeds a threshold value, store the particular agent in an agent repository for future utilization.

710. The system of claim 709, wherein creating specialized processing agents comprises: generating clarification prompts based on assumptions in the decomposition opportunities; presenting the clarification prompts to a user via an output interface; receiving clarification responses from the user; and refining the specialized processing agents based on the clarification responses.

711. A computer-implemented method for routing in an artificial intelligence model unification and access environment, the method comprising: receiving, by one or more processors, a user input via an input interface;Atty Dkt: 11016-8049PCT calculating, by the one or more processors, input characteristics data by extracting metadata from the user input, wherein the input characteristics data comprises at least one of: key terms, temporal context, or complexity metric; determining, by the one or more processors, configmation parameters for input processing based at least in part on the input characteristics data; selecting, by the one or more processors, a first set of processing resources from a plurality of available processing resources based at least in part on the input characteristics data and historical performance data; decomposing, by the one or more processors, the user input into a plurality of sub-inputs when decomposition logic indicates decomposition is appropriate; issuing, by the one or more processors, the user input or the plurality of sub-inputs to the first set of processing resources; receiving, by the one or more processors, a first set of outputs from the first set of processing resources; generating, by the one or more processors, validation data comprising challenge information configured to test accuracy of the first set of outputs; issuing, by the one or more processors, the validation data to the first set of processing resources to generate refined outputs; when consensus logic is enabled, obtaining, by the one or more processors, ranking data from the first set of processing resources, wherein each processing resource in the first set ranks the refined outputs based on quality metrics; calculating, by the one or more processors, weighted scores for the refined outputs based on the ranking data; and selecting, by the one or more processors, a final output from the refined outputs based on the weighted scores.

712. The method of claim 711, further comprising: determining a resource allocation strategy based on budget constraint parameters and current expenditure data; and adjusting the determination of configmation parameters based on the resource allocation strategy.

713. The method of claim 711, wherein calculating the input characteristics data comprises: sending a metadata extraction request to a selected processing resource from the plurality of available processing resources; receiving metadata response data from the selected processing resource; and parsing the metadata response data to extract the key terms, the temporal context, and the complexity metric.

714. The method of claim 711, wherein decomposing the user input comprises: sending a decomposition request to at least one processing resource in the first set of processing resources, wherein the decomposition request instructs the at least one processing resource to break down the user input into a series of interconnected sub-inputs; and receiving decomposition output data comprising the plurality of sub-inputs.

715. The method of claim 714, further comprising: when validation logic for input processing is enabled:Atty Dkt: 11016-8049PCT sending validation prompts to the at least one processing resource to challenge accuracy of the decomposition output data; and updating the plurality of sub-inputs based on validation responses received from the at least one processing resource.

716. The method of claim 711, further comprising: maintaining performance tracking data associating input characteristics with processing resource performance metrics; updating the performance tracking data based on the ranking data and the weighted scores; and utilizing the updated performance tracking data when selecting processing resources for subsequent user inputs.

717. The method of claim 711, further comprising: when agentic processing logic is enabled: analyzing the user input to identify decomposition opportunities; creating specialized processing agents configured to handle specific sub-tasks identified in the decomposition opportunities; tracking frequency of agent usage; and when frequency of a particular agent exceeds a threshold value, storing the particular agent in an agent repository for future utilization.

718. The method of claim 717, wherein creating specialized processing agents comprises: generating clarification prompts based on assumptions in the decomposition opportunities; presenting the clarification prompts to a user via an output interface; receiving clarification responses from the user; and refining the specialized processing agents based on the clarification responses.

719. The method of claim 711, wherein the quality metrics comprise at least two of: accuracy measure, clarity measure, conciseness measure, confidence level, or latency value.

720. The method of claim 711, further comprising: storing the historical performance data in a data store, wherein the historical performance data associates input characteristics with performance scores for each processing resource in the plurality of available processing resources.SETTINGS721. A configuration system for artificial intelligence model unification and access, the system comprising: one or more processors; and one or more memories storing instructions that, when executed by the one or more processors, cause the system to: maintain a plurality of configmation parameters, wherein the plurality of configuration parameters comprises: budget constraint parameters defining expenditure limits for resource usage; quality tier parameters defining quality levels for selection of processing resources; sophistication parameters defining processing depth for at least one of input processing or output processing;Atty Dkt: 11016-8049PCT automation control parameters defining whether configuration selection is automated or manual; feature enablement parameters defining which processing features are activated; and verbosity parameters defining detail level for outputs generated by processing resources; receive input characteristics data associated with a user input; when automation control parameters indicate automated configuration: calculate optimized configuration values for the plurality of configuration parameters based at least in part on the input characteristics data; and apply the optimized configuration values to control routing of the user input to processing resources; when automation control parameters indicate manual configuration: retrieve user-specified configuration values for the plurality of configuration parameters; and apply the user-specified configuration values to control routing of the user input to processing resources; monitor current expenditure data against the budget constraint parameters; and when current expenditure data exceeds the budget constraint parameters, generate an alert and prevent further processing unless override authorization is received.

722. The system of claim 721, wherein the budget constraint parameters comprise: a daily expenditure limit value that resets at a predetermined time; and an override setting that, when activated, allows a user to reset the daily expenditure limit value.

723. The system of claim 721, wherein the quality tier parameters comprise a stratified set of quality levels, each quality level associated with a different cost range and performance characteristic.

724. The system of claim 723, wherein the stratified set of quality levels comprises: a first level corresponding to lowest-cost processing resources; a second level corresponding to economical processing resources providing improved performance relative to the first level; a third level corresponding to moderate-cost processing resources providing balanced performance and cost; and a fourth level corresponding to premium processing resources providing highest performance.

725. The system of claim 721, wherein the sophistication parameters comprise: input processing sophistication values defining a number of processing resources to utilize for input processing; and output processing sophistication values defining a number of processing resources to utilize for output processing.

726. The system of claim 725, wherein the input processing sophistication values range from a low setting utilizing a single processing resource to a high setting utilizing multiple processing resources with consensus ranking.

727. The system of claim 721, wherein the feature enablement parameters comprise: decomposition enablement indicating whether to decompose inputs into sub-inputs; challenge enablement indicating whether to challenge outputs with validation data;Atty Dkt: 11016-8049PCT consensus enablement indicating whether to obtain ranking data from multiple processing resources; agentic enablement indicating whether to utilize agent-based processing; and historical ranking enablement indicating whether to use historical performance data for resource selection.

728. The system of claim 721, wherein calculating the optimized configuration values comprises: determining a complexity metric from the input characteristics data; accessing a configmation matrix that maps combinations of the complexity metric, the quality tier parameters, and the sophistication parameters to specific feature enablement states; and extracting the optimized configuration values from the configuration matrix.

729. The system of claim 721, wherein the verbosity parameters comprise levels ranging from minimal detail with no explanatory context to maximum detail with comprehensive explanations and references.

730. The system of claim 721, wherein the instructions further cause the system to: provide a user interface enabling a user to override global configuration settings on a per-request basis; and log override events including user identity, timestamp, and overridden settings.

731. A computer-implemented method for configuration management in an artificial intelligence model unification and access environment, the method comprising: maintaining, by one or more processors, a plurality of configuration parameters, wherein the plurality of configuration parameters comprises: budget constraint parameters defining expenditure limits for resource usage; quality tier parameters defining quality levels for selection of processing resources; sophistication parameters defining processing depth for at least one of input processing or output processing; automation control parameters defining whether configuration selection is automated or manual; feature enablement parameters defining which processing features are activated; and verbosity parameters defining detail level for outputs generated by processing resources; receiving, by the one or more processors, input characteristics data associated with a user input; when automation control parameters indicate automated configuration: calculating, by the one or more processors, optimized configuration values for the plurality of configmation parameters based at least in part on the input characteristics data; and applying, by the one or more processors, the optimized configuration values to control routing of the user input to processing resources; when automation control parameters indicate manual configuration: retrieving, by the one or more processors, user-specified configuration values for the plurality of configmation parameters; and applying, by the one or more processors, the user-specified configmation values to control routing of the user input to processing resources; monitoring, by the one or more processors, current expenditure data against the budget constraint parameters; and when current expenditure data exceeds the budget constraint parameters, generating, by the one or more processors, an alert and preventing further processing unless override authorization is received.

732. The method of claim 731, wherein the budget constraint parameters comprise:Atty Dkt: 11016-8049PCT a daily expenditure limit value that resets at a predetermined time; and an override setting that, when activated, allows a user to reset the daily expenditure limit value.

733. The method of claim 731, wherein the quality tier parameters comprise a stratified set of quality levels, each quality level associated with a different cost range and performance characteristic.

734. The method of claim 731, wherein calculating the optimized configuration values comprises: determining a complexity metric from the input characteristics data; accessing a configuration matrix that maps combinations of the complexity metric, the quality tier parameters, and the sophistication parameters to specific feature enablement states; and extracting the optimized configuration values from the configuration matrix.

735. The method of claim 731, wherein the feature enablement parameters comprise: decomposition enablement indicating whether to decompose inputs into sub-inputs; challenge enablement indicating whether to challenge outputs with validation data; consensus enablement indicating whether to obtain ranking data from multiple processing resources; agentic enablement indicating whether to utilize agent-based processing; and historical ranking enablement indicating whether to use historical performance data for resource selection.

736. The method of claim 731, further comprising: providing a user interface enabling a user to override global configmation settings on a per-request basis; and logging override events including user identity, timestamp, and overridden settings.

737. The method of claim 731, further comprising: dynamically adjusting the budget constraint parameters based on real-time expenditure tracking and remaining budget availability.

738. The method of claim 731, wherein the sophistication parameters comprise: input processing sophistication values defining a number of processing resources to utilize for input processing; and output processing sophistication values defining a number of processing resources to utilize for output processing.

739. The method of claim 738, wherein the input processing sophistication values range from a low setting utilizing a single processing resource to a high setting utilizing multiple processing resources with consensus ranking.

740. The method of claim 731, wherein the verbosity parameters comprise levels ranging from minimal detail with no explanatory context to maximum detail with comprehensive explanations and references.DATA741. A data management system for artificial intelligence model unification and access, the system comprising: one or more processors; and one or more memories storing instructions that, when executed by the one or more processors, cause the system to: generate and store input decomposition data comprising a plurality of sub-inputs derived from decomposition of a user input; generate and store validation data comprising challenge information configured to test accuracy of outputs from processing resources;Atty Dkt: 11016-8049PCT generate and store input characteristic data comprising metadata extracted from the user input, wherein the input characteristic data includes: key term data identifying significant terms in the user input; temporal reference data indicating temporal context of the user input; and complexity data indicating a difficulty level of the user input; maintain resource identification data comprising: input processing resource identifiers for processing resources selected for input processing; output processing resource identifiers for processing resources selected for output processing; and validation resource identifiers for processing resources selected for validation operations; store processed input data representing a final processed version of the user input after input processing operations; store output data representing responses generated by the output processing resource identifiers; store final result data representing a selected output after ranking and validation operations; maintain cost information data associating each processing resource with cost metrics; maintain context storage data configured to optimize input capacity limitations of processing resources; and maintain historical performance data associating input characteristics with performance rankings for processing resources.

742. The system of claim 741, wherein the instructions further cause the system to: receive a new user input; extract new input characteristic data from the new user input; query the historical performance data using the new input characteristic data to identify highest-ranked processing resources for the new user input; and select processing resources for the new user input based on the highest-ranked processing resources identified from the historical performance data.

743. The system of claim 741, wherein the complexity data comprises a numerical value on a scale from 1 to 5, wherein 1 represents lowest complexity and 5 represents highest complexity.

744. The system of claim 741, wherein the temporal reference data comprises an enumeration indicating whether the user input relates to past events, present events, or future events.

745. The system of claim 741, wherein the validation data comprises: a plurality of challenge questions configured to verify accuracy of outputs generated by processing resources; and validation criteria for evaluating responses to the plurality of challenge questions.

746. The system of claim 741, wherein the input decomposition data comprises: a sequence of interconnected sub-inputs, each sub-input representing a portion of the user input; and dependency information indicating relationships between the interconnected sub-inputs.

747. The system of claim 746, wherein the dependency information indicates whether sub-inputs should be processed synchronously or asynchronously.Atty Dkt: 11016-8049PCT748. The system of claim 741, wherein the context storage data comprises: a transient cache of information larger than a context capacity of individual processing resources; embedding data representing vectorized content from prior interactions; and relevance scores indicating which cached information is most pertinent for a current user input.

749. The system of claim 748, wherein the instructions further cause the system to: when a processing resource approaches its context capacity limit: identify high-relevance information from the context storage data; reload the high-relevance information into an input sent to the processing resource; and remove low-relevance information from the input to maintain the input within the context capacity limit.

750. The system of claim 741, wherein the cost information data comprises: absolute cost values for each processing resource; and relative cost values comparing processing resources within quality tiers.

751. A computer-implemented method for data management in an artificial intelligence model unification and access environment, the method comprising: generating and storing, by one or more processors, input decomposition data comprising a plurality of subinputs derived from decomposition of a user input; generating and storing, by the one or more processors, validation data comprising challenge information configured to test accuracy of outputs from processing resources; generating and storing, by the one or more processors, input characteristic data comprising metadata extracted from the user input, wherein the input characteristic data includes: key term data identifying significant terms in the user input; temporal reference data indicating temporal context of the user input; and complexity data indicating a difficulty level of the user input; maintaining, by the one or more processors, resource identification data comprising: input processing resource identifiers for processing resources selected for input processing; output processing resource identifiers for processing resources selected for output processing; and validation resource identifiers for processing resources selected for validation operations; storing, by the one or more processors, processed input data representing a final processed version of the user input after input processing operations; storing, by the one or more processors, output data representing responses generated by the output processing resource identifiers; storing, by the one or more processors, final result data representing a selected output after ranking and validation operations; maintaining, by the one or more processors, cost information data associating each processing resource with cost metrics; maintaining, by the one or more processors, context storage data configured to optimize input capacity limitations of processing resources; and maintaining, by the one or more processors, historical performance data associating input characteristics with performance rankings for processing resources.Atty Dkt: 11016-8049PCT752. The method of claim 751, further comprising: receiving a new user input; extracting new input characteristic data from the new user input; querying the historical performance data using the new input characteristic data to identify highest-ranked processing resources for the new user input; and selecting processing resources for the new user input based on the highest-ranked processing resources identified from the historical performance data.

753. The method of claim 751, wherein the complexity data comprises a numerical value on a scale from 1 to 5, wherein 1 represents lowest complexity and 5 represents highest complexity.

754. The method of claim 751, wherein the validation data comprises: a plurality of challenge questions configured to verify accuracy of outputs generated by processing resources; and validation criteria for evaluating responses to the plurality of challenge questions.

755. The method of claim 751, wherein the input decomposition data comprises: a sequence of interconnected sub-inputs, each sub-input representing a portion of the user input; and dependency information indicating relationships between the interconnected sub-inputs.

756. The method of claim 755, wherein the dependency information indicates whether sub-inputs should be processed synchronously or asynchronously.

757. The method of claim 751, wherein the context storage data comprises: a transient cache of information larger than a context capacity of individual processing resources; embedding data representing vectorized content from prior interactions; and relevance scores indicating which cached information is most pertinent for a current user input.

758. The method of claim 757, further comprising: when a processing resource approaches its context capacity limit: identifying high-relevance information from the context storage data; reloading the high-relevance information into an input sent to the processing resource; and removing low-relevance information from the input to maintain the input within the context capacity limit.

759. The method of claim 751, further comprising: updating the historical performance data after each processing operation based on: ranking scores received from processing resources; quality metrics of final outputs; and user feedback data when available.

760. The method of claim 751, wherein the temporal reference data comprises an enumeration indicating whether the user input relates to past events, present events, or future events.INVESTMENT ADVISORY761. An investment advisory system utilizing artificial intelligence model unification and access, the system comprising: one or more processors; andAtty Dkt: 11016-8049PCT one or more memories storing instructions that, when executed by the one or more processors, cause the system to: receive client financial data comprising at least one of: risk profile information, investment goal information, current portfolio information, or financial constraint information; generate an advisory input by combining the client financial data with market data obtained from one or more market data sources; route the advisory input to a plurality of artificial intelligence processing resources selected based on historical performance data for investment advisory tasks; receive a plurality of investment recommendation outputs from the plurality of artificial intelligence processing resources; generate validation data comprising challenge questions configured to test accuracy and suitability of the plurality of investment recommendation outputs; send the validation data to the plurality of artificial intelligence processing resources to generate refined investment recommendation outputs; obtain ranking data from the plurality of artificial intelligence processing resources, wherein each processing resource ranks the refined investment recommendation outputs based on quality metrics; calculate weighted scores for the refined investment recommendation outputs based on the ranking data; select a final investment recommendation from the refined investment recommendation outputs based on the weighted scores; and generate a presentation of the final investment recommendation for delivery to a client interface.

762. The system of claim 761, wherein the plurality of artificial intelligence processing resources are selected based on: metadata extracted from the advisory input indicating investment domain, time horizon, and complexity level; and historical performance rankings associating processing resources with performance in the investment domain.

763. The system of claim 761, wherein the market data comprises at least one of: real-time stock prices, bond yields, commodity prices, market trend data, or economic indicator data.

764. The system of claim 761, wherein generating the advisory input further comprises: decomposing the client financial data into a plurality of sub-inquiries related to different aspects of investment strategy; and generating separate advisory inputs for each sub-inquiry.

765. The system of claim 764, wherein the plurality of sub-inquiries comprise at least two of: asset allocation analysis, risk assessment, tax optimization analysis, or rebalancing strategy.

766. The system of claim 761, wherein the quality metrics comprise at least two of: alignment with risk profile, expected return optimization, diversification measure, regulatory compliance measure, or tax efficiency measure.

767. The system of claim 761, wherein the instructions further cause the system to: monitor ongoing market conditions;Atty Dkt: 11016-8049PCT when market conditions change beyond a threshold level, automatically generate updated advisory inputs; and obtain updated investment recommendations based on the updated advisory inputs.

768. The system of claim 761, wherein the instructions further cause the system to: extract knowledge from proprietary financial documents using a knowledge graph retrieval-augmented generation system; ground the plurality of artificial intelligence processing resources with the extracted knowledge; and utilize the grounded processing resources to generate the plurality of investment recommendation outputs.

769. The system of claim 761, wherein the final investment recommendation comprises: recommended portfolio adjustments; expected return projections; risk assessment metrics; and rationale explaining the recommended portfolio adjustments.

770. The system of claim 761, wherein the instructions further cause the system to: track performance of implemented investment recommendations over time; update the historical performance data based on actual performance relative to projected performance; and adjust selection of artificial intelligence processing resources for future advisory inputs based on the updated historical performance data.

771. A computer-implemented method for investment advisory utilizing artificial intelligence model unification and access, the method comprising: receiving, by one or more processors, client financial data comprising at least one of: risk profile information, investment goal information, current portfolio information, or financial constraint information; generating, by the one or more processors, an advisory input by combining the client financial data with market data obtained from one or more market data sources; routing, by the one or more processors, the advisory input to a plurality of artificial intelligence processing resources selected based on historical performance data for investment advisory tasks; receiving, by the one or more processors, a plurality of investment recommendation outputs from the plurality of artificial intelligence processing resources; generating, by the one or more processors, validation data comprising challenge questions configmed to test accuracy and suitability of the plurality of investment recommendation outputs; sending, by the one or more processors, the validation data to the plurality of artificial intelligence processing resources to generate refined investment recommendation outputs; obtaining, by the one or more processors, ranking data from the plurality of artificial intelligence processing resources, wherein each processing resource ranks the refined investment recommendation outputs based on quality metrics; calculating, by the one or more processors, weighted scores for the refined investment recommendation outputs based on the ranking data; selecting, by the one or more processors, a final investment recommendation from the refined investment recommendation outputs based on the weighted scores; andAtty Dkt: 11016-8049PCT generating, by the one or more processors, a presentation of the final investment recommendation for delivery to a client interface.

772. The method of claim 771, wherein the plurality of artificial intelligence processing resources are selected based on: metadata extracted from the advisory input indicating investment domain, time horizon, and complexity level; and historical performance rankings associating processing resources with performance in the investment domain.

773. The method of claim 771, further comprising: decomposing the client financial data into a plurality of sub-inquiries related to different aspects of investment strategy; and generating separate advisory inputs for each sub-inquiry.

774. The method of claim 773, wherein the plurality of sub-inquiries comprise at least two of: asset allocation analysis, risk assessment, tax optimization analysis, or rebalancing strategy.

775. The method of claim 771, wherein the quality metrics comprise at least two of: alignment with risk profile, expected return optimization, diversification measure, regulatory compliance measure, or tax efficiency measure.

776. The method of claim 771, further comprising: monitoring ongoing market conditions; when market conditions change beyond a threshold level, automatically generating updated advisory inputs; and obtaining updated investment recommendations based on the updated advisory inputs.

777. The method of claim 771, further comprising: extracting knowledge from proprietary financial documents using a knowledge graph retrieval-augmented generation system; grounding the plurality of artificial intelligence processing resources with the extracted knowledge; and utilizing the grounded processing resources to generate the plurality of investment recommendation outputs.

778. The method of claim 771, further comprising: tracking performance of implemented investment recommendations over time; updating the historical performance data based on actual performance relative to projected performance; and adjusting selection of artificial intelligence processing resources for future advisory inputs based on the updated historical performance data.

779. The method of claim 771, wherein the market data comprises at least one of: real-time stock prices, bond yields, commodity prices, market trend data, or economic indicator data.

780. The method of claim 771, wherein the final investment recommendation comprises: recommended portfolio adjustments; expected return projections; risk assessment metrics; and rationale explaining the recommended portfolio adjustments.

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