Foundation Model Switchboard Routing for Enterprise AI Integration
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Solution Overview
Problem
Integrating generative AI foundation models into enterprise platforms is challenging due to technical complexities, varying strengths and weaknesses of models, lack of standardization, and inefficient resource utilization, leading to significant experimentation and resource waste.
Innovation Solution
A switchboard platform that enables intelligent access, routing, and orchestration of foundation models based on policy parameters, model economics, performance expectations, and security considerations, optimizing resource use and model selection for context-specific tasks.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Adaptability or versatility
If multiple foundation models are integrated into enterprise platforms, then model capabilities and versatility are improved, but device complexity and integration difficulty increase
Solution Approach 1:
The patent introduces a switchboard platform as an intermediary layer between enterprise applications and multiple foundation models. This switchboard manages model selection, routing, and orchestration, shielding applications from the complexity of directly integrating multiple models. The switchboard evaluates model performance, handles model-specific configurations, and provides a unified interface, thereby resolving the contradiction by maintaining high model versatility while abstracting integration complexity.
Solution Approach 2:
The switchboard platform is designed as a universal system that can handle multiple types of foundation models (e.g., LLMs, image generators, audio models) through a single unified interface. It provides multi-functional capabilities including model registration, performance evaluation, dynamic routing, and resource management, allowing enterprises to leverage diverse model capabilities without creating separate integration pathways for each model type.
2Measurement precision
If foundation models are selected based on performance optimization, then task accuracy is improved, but resource consumption and costs increase
Solution Approach 1:
The patent implements dynamic model selection where the switchboard continuously evaluates foundation model performance against specific task requirements and adjusts model deployment in real-time. Performance metrics are monitored and used to dynamically route tasks to the most appropriate models, balancing accuracy requirements with resource efficiency. This dynamic approach allows the system to optimize for high accuracy when necessary while selecting more resource-efficient models for simpler tasks.
Solution Approach 2:
The switchboard platform modifies operational parameters by adjusting model selection criteria, routing decisions, and resource allocation based on changing task requirements and performance feedback. It changes parameters such as model temperature settings, token limits, and parallel processing configurations to optimize the balance between task accuracy and resource consumption for different workloads.
3Productivity
If foundation models are deployed across multiple enterprises, then scalability and resource sharing are improved, but security risks and access control complexity increase
Solution Approach 1:
The patent segments the switchboard platform into modular components with distinct security responsibilities. It implements separate authentication modules, authorization layers, and isolation mechanisms for different enterprises and models. This segmentation allows multiple enterprises to share foundation models through a common platform while maintaining independent security boundaries, thus achieving scalability without compromising security.
Solution Approach 2:
The switchboard acts as a secure intermediary between enterprises and foundation models, implementing centralized access control, authentication, and authorization mechanisms. It mediates all interactions, enforcing security policies, managing credentials, and isolating enterprises from direct access to model infrastructure. This intermediary layer enables scalable multi-enterprise deployment while maintaining robust security controls.
Data Source
AI summary
Implementations of the present disclosure provide receiving, by an intelligent router of the switchboard platform, a first request from an application, the first request including at least a portion of a prompt and a set of policy parameters, selecting, by the intelligent router, a foundation model of a sub-set of foundation models at least partially based on at least one policy parameter in the set of policy parameters, determining, from a model registry of the switchboard platform, connection data for the foundation model, transmitting, by the intelligent router and through a model connector of the switchboard platform, a second request for processing by the foundation model, the second request being transmitted using the connection data and including at least a portion of the prompt, receiving, by the intelligent router, a response from the foundation model, and transmitting the response to the application.