System for multi-tenant language AI with retrieval-based knowledge isolation and context injection

Abstract

A computer-implemented system for operating a multi-tenant, language-capable artificial intelligence platform, wherein the system comprises the following: a backend orchestration layer (101) configured to receive user input via a voice or text interface and resolve a tenant identifier associated with the user input; a plurality of client-isolated vector knowledge stores (102), each corresponding to a respective client and configured to store vector embeddings of client-specific data; a context injection module (103) configured to retrieve client-specific metadata, agent configuration and conversation status prior to retrieving the document and integrate it into a processing pipeline; a retrieval engine (104) configured to query only a corresponding tenant-isolated vector knowledge store based on the tenant identifier in order to obtain relevant document vectors using similarity assessment and policy-based filtering; a dynamic prompt assembly module (105) configured to generate a prompt by combining retrieved document vectors, tenant-specific metadata, agent configuration, and user input; and a language model interface (106) configured to generate a response based on the input prompt and deliver the response via a speech or text channel, thereby ensuring tenant-specific, context-aware and secure interaction.

Description

Technical field of expertise:

[0001] The present invention relates to the field of artificial intelligence and cloud-based computing systems. More specifically, the invention relates to a system and a method for operating multi-tenant, voice-controlled conversational agents using retrieval-augmented generation (RAG), tenant-isolated knowledge stores, and dynamic context injection. The invention also relates to scalable speech and text interaction systems integrated into telephony infrastructure that enable secure, domain-specific, and policy-driven AI communication across multiple organizational tenants. Background of the invention;

[0002] With the rapid advancement of artificial intelligence, particularly large language models (LLMs), conversational agents are becoming increasingly common in enterprise applications such as customer service, healthcare, and automated support systems. These systems are often enhanced through retrieval-augmented generation (RAG), which allows the integration of external knowledge sources into language model responses, thereby improving accuracy, relevance, and contextual awareness. Despite these advancements, however, existing systems face significant challenges when deployed in multi-tenant environments.

[0003] In traditional architectures, multiple organizations or tenants share a common infrastructure, including databases and AI models. Many existing RAG-based systems rely on shared vector databases where tenant-specific data is distinguished only by metadata tags or filtering mechanisms. Such approaches are inherently limited, as they increase the risk of data leaks, cross-tenant interference, and unauthorized access to sensitive information. Ensuring strict data isolation is particularly important in sectors such as healthcare, finance, and legal services, where stringent confidentiality and compliance requirements apply.

[0004] Furthermore, traditional systems often use static or manually configured prompts to generate responses. These prompts do not dynamically adapt to tenant-specific requirements, such as organizational policies, domain restrictions, or evolving conversation contexts. This can result in generated responses that are irrelevant, violate internal policies, or fail to reflect the intended personality of the conversation partner. Additionally, the lack of systematic context injection mechanisms prevents efficient use of tenant metadata and conversation history during both the retrieval and generation phases.

[0005] Another major limitation lies in the scalability of existing solutions. Many systems require separate infrastructure stacks or duplicate pipelines for each tenant, resulting in increased operational complexity, higher costs, and reduced maintainability. This approach is inefficient, especially for service providers who want to onboard a large number of tenants while ensuring consistent performance and security.

[0006] Voice-based AI systems present additional challenges. Integration with telephone infrastructure, including public switched telephone networks (PSTN) and Voice over IP (VoIP) systems, requires real-time processing of voice input and output. Existing voice pipelines are often tightly coupled and lack flexibility, making it difficult to support multiple tenants, agents, or dynamic configurations. Furthermore, updating knowledge bases in real time without downtime remains a persistent problem, limiting the system's ability to provide up-to-date information.

[0007] Previous solutions have attempted to address some of these challenges by introducing multi-tenant conversational frameworks or improving retrieval techniques. However, they do not offer a comprehensive approach that combines strict knowledge isolation at the tenant level, dynamic context injection prior to retrieval, and seamless integration with voice-based communication systems.

[0008] Therefore, there is a need for an improved system that enables the secure, scalable, and efficient operation of multi-tenant, language-enabled AI agents. Such a system should ensure strict separation of tenant knowledge, dynamically integrate contextual information into the retrieval and generation pipeline, and support real-time speech and text interactions without requiring infrastructure duplication. The present invention addresses these challenges by introducing a novel architecture that integrates tenant-isolated knowledge repositories with context-sensitive, retrieval-enhanced generation within a unified and scalable framework. Objectives of the invention 1. Provision of a system to enable secure, language-based AI interactions with multiple clients and strict isolation of client-specific knowledge repositories. 2. The goal is to develop a dynamic context injection mechanism that incorporates client metadata, policies, and conversation status into the pipeline for request-enhanced generation. 3. To enable scalable deployment of multiple voice agents across tenants without requiring duplication of infrastructure or pipelines. 4. To facilitate real-time knowledge acquisition and seamless integration with telephony and text communication channels. Summary of the invention:

[0009] The present invention discloses a system and a method for operating multi-tenant, language-capable artificial intelligence agents using a retrieval-enhanced generation framework with improved security, scalability, and contextual intelligence. The system is designed to overcome the limitations of conventional AI architectures by introducing tenant-specific knowledge isolation combined with dynamic context injection, thereby enabling highly customized and secure conversational experiences across multiple organizations.

[0010] In one embodiment, the system establishes a multi-tenant architecture in which each tenant is assigned to one or more dedicated vector knowledge stores. These knowledge stores are physically and logically isolated to ensure that data belonging to one tenant cannot be accessed or derived by another. The system automatically provisions and manages these knowledge stores during tenant onboarding, eliminating the need for manual infrastructure duplication while maintaining strict data segregation.

[0011] A key feature of the invention lies in its context injection mechanism, which operates before the retrieval phase of the RAG pipeline. Upon receiving a user request, the system identifies the corresponding tenant and retrieves relevant metadata, including organizational policies, domain-specific restrictions, agent personality configurations, and conversation history. This information is structured into a context object and inserted into the processing pipeline before a document retrieval occurs. By influencing both the retrieval strategy and the timely construction, the system ensures that the responses are tailored to tenant-specific requirements and restrictions.

[0012] The retrieval process is performed exclusively within the tenant's isolated knowledge base. To identify relevant document segments while excluding content that does not conform to predefined rules, advanced retrieval techniques are employed, including similarity scoring and policy-based filtering. The retrieved information is then combined with system-level instructions, tenant context, and user input to dynamically construct a prompt for the language model. This dynamic prompt assembly enables the system to generate context-aware, policy-compliant, and domain-specific responses in real time.

[0013] The invention also integrates speech processing functions and enables seamless interaction via telephone systems such as PSTN and VoIP. Incoming voice input is converted to text using speech-to-text technology and processed via the RAG pipeline. The generated responses are then converted back into speech using text-to-speech synthesis and returned to the user. This unified processing framework supports both voice and text channels and ensures consistent behavior across different communication modes.

[0014] Furthermore, the system supports real-time knowledge updates, allowing tenants to upload or modify documents without system downtime. The ingestion pipeline processes new data by generating embeds and updating the corresponding vector stores to ensure the latest information is immediately available.

[0015] Overall, the invention offers a robust and scalable solution for the deployment of AI-driven, multi-tenant speech agents. By combining tenant-isolated knowledge management, dynamic context injection, and integrated speech capabilities, the system significantly improves data security, responsiveness, and operational efficiency, making it suitable for a wide range of enterprise applications. Brief description of the drawing Fig. shows a block diagram of the system according to the invention. Detailed description of the invention

[0016] The present invention relates to a system and a method for operating a multi-tenant, voice-controlled artificial intelligence platform that utilizes Retrieval-Augmented Generation (RAG) in combination with tenant-isolated knowledge stores and dynamic context injection. The invention is intended to enable scalable, secure, and context-sensitive conversational agents that can interact via both voice and text channels, while ensuring strict separation of tenant data and adaptive response generation tailored to organizational policies and domain-specific requirements.

[0017] In one embodiment, the system comprises a distributed architecture including a front-end interface, a back-end orchestration layer, a retrieval-enhanced generation pipeline engine, tenant-isolated vector knowledge stores, and a telephony and voice integration layer. These components work in a coordinated manner to process user interactions, retrieve relevant knowledge, and generate contextually appropriate responses in real time.

[0018] The frontend interface provides a web-based or application-based environment through which administrators and operators connected to various tenants can configure and manage their respective AI agents. This interface includes modules for user authentication, role-based access control, document uploading, policy definition, and agent personality configuration. The system allows tenants to define domain-specific parameters such as permitted topics, compliance restrictions, escalation rules, and conversational tone, which are then used during the response generation process.

[0019] The backend orchestration layer acts as the system's central control mechanism. It comprises an API (Application Programming Interface) gateway, request routing services, tenant resolution modules, and agent lifecycle management components. When a user interaction is received, either via a voice call or a text-based interface, the backend layer first processes the incoming request to identify the corresponding tenant. Tenant identification can be achieved through several mechanisms, including authentication tokens, API keys, request headers, subdomain mappings, or telephony metadata such as dialed numbers, session identifiers, or routing rules. Once the tenant is identified, the backend retrieves the appropriate tenant-specific configuration and initializes a processing session.

[0020] A key feature of the invention is the implementation of tenant-isolated vector knowledge stores. Each tenant is linked to one or more dedicated vector databases or collections that store embeds derived from tenant-specific documents and data sources. These knowledge stores can be further segmented within the tenant on an agent-by-agent or domain-by-domain basis. Isolation is enforced at both the physical and logical levels, ensuring that queries executed for one tenant cannot access or derive information from another tenant's data. This strict separation overcomes the limitations of conventional systems based on shared databases with metadata filtering, thereby improving data security and regulatory compliance.

[0021] The system includes a data ingestion pipeline that allows tenants to upload, update, and manage their knowledge base content. The ingestion process begins with document capture, which can be done through manual uploads, integration with external data sources, or automated synchronization processes. The captured documents then undergo preprocessing, including format normalization, removal of irrelevant content, and segmentation into semantically meaningful blocks. Each block is subsequently transformed into a vector embed using an embedding model. These embeds, along with associated metadata such as source, timestamp, and classification tags, are stored in the tenant's dedicated vector knowledge base.The system is capable of performing this recording process in real time, allowing new or updated information to be integrated without requiring system downtime.

[0022] Another important aspect of the invention is the context injection mechanism, which operates before the RAG pipeline's retrieval phase. After resolving the tenant identity, the system retrieves a number of context-related elements, including tenant metadata, organizational policies, agent configuration, and conversation status. Tenant metadata can include information such as the organization's domain, compliance requirements, preferred response style, and operational constraints. The agent configuration can define persona attributes such as tone, language preferences, and permissible topics. The conversation status encompasses historical interactions within the current session and enables the system to maintain continuity and coherence in multi-round conversations.

[0023] These context elements are combined into a structured context object by a context injection module. This context object is then integrated into the processing pipeline before a document retrieval is performed. By injecting context at this stage, the system ensures that subsequent retrieval and generation processes are influenced by tenant-specific requirements. For example, policy restrictions derived from tenant metadata can limit the types of documents that can be retrieved or modify the evaluation of retrieved content. Similarly, agent personality definitions can influence the style and structure of the generated response.

[0024] The pipeline engine for query-enhanced generation is responsible for executing the core processing steps required to generate responses. Once the context object is prepared, the system computes an embed for the user query or transcribed speech input. This query embed is used to perform a similarity search within the tenant-specific vector knowledge base. The retrieval process is limited exclusively to the data of the respective tenant and ensures that no information shared across tenants is retrieved.

[0025] The system retrieves a series of candidate document blocks based on similarity scores between the query embed and stored embeds. These candidate results are further refined through policy-based filtering and scoring mechanisms. The filtering process removes any content that violates tenant-defined policies, such as restricted topics or outdated information. The scoring mechanism can adjust similarity scores based on additional factors, including document recency, source reliability, and contextual relevance. This policy-aware retrieval process ensures that only appropriate and high-quality information is selected for inclusion in the response.

[0026] After the request, the system performs dynamic prompt assembly. Unlike conventional systems based on static templates, the present invention dynamically generates prompts for each request. The prompt comprises several components, such as basic system instructions that govern safe and compliant behavior, client-specific context derived from the context object, retrieved document excerpts, relevant conversation history, and the current user query. The integration of these components ensures that the language model receives comprehensive and context-aware input, enabling it to generate accurate and relevant responses.

[0027] The assembled input request is then passed to a language model interface, which interacts with one or more major language models to generate a response. The system can dynamically select the most appropriate language model based on factors such as tenant preferences, latency requirements, cost considerations, and query complexity. The generated response is returned in text format and reflects the combined influence of the retrieved knowledge, tenant context, and system-level instructions.

[0028] In addition to text-based interactions, the invention supports voice-based communication through an integrated telephony and speech processing layer. This layer enables the system to handle incoming and outgoing voice calls using technologies such as public telephone networks and Voice over IP (VoIP) systems. When a voice interaction is initiated, the system establishes a session and processes the incoming audio stream using speech-to-text (STT) technology. The transcribed text is then passed to the RAG pipeline as a user query.

[0029] Once the response is generated, the system uses text-to-speech (TTS) technology to convert the text output into synthesized speech. The audio response is then transmitted back to the user in real time via the telephone infrastructure. This seamless integration of STT, RAG processing, and TTS enables natural and interactive voice conversations. Furthermore, the same underlying RAG pipeline can be used for non-voice channels such as chat interfaces and messaging platforms, ensuring consistent behavior across different communication modalities.

[0030] The system also includes mechanisms for continuous learning and real-time updates. When new documents are added or existing documents are modified, the ingestion pipeline processes the changes and updates the corresponding vector knowledge stores. These updates are immediately available for subsequent retrievals, allowing the system to provide current information without service interruption. This capability is particularly valuable in dynamic environments where knowledge bases are frequently updated.

[0031] Security and isolation are fundamental aspects of the invention. In addition to isolating knowledge repositories, the system enforces separation at the inference level by ensuring that input prompts and generated responses depend solely on tenant-specific data and contexts. Access control mechanisms are implemented throughout the system to prevent unauthorized access and ensure the confidentiality of each tenant's data. The architecture is designed for operation in a multi-tenant cloud environment where infrastructure resources are shared while maintaining strict logical separation between data and processing.

[0032] The invention also offers scalability and flexibility by supporting multiple agents within a single client, each with different configurations and knowledge domains. This allows companies to deploy specialized agents for various use cases, such as customer support, technical assistance, or internal knowledge management. The system can dynamically select the appropriate agent and associated knowledge repository based on the context of the interaction.

[0033] Overall, the present invention introduces a novel approach to multi-tenant conversational AI by combining tenant-isolated knowledge management, dynamic context injection, policy-aware retrieval, and integrated natural language processing within a unified framework. The system overcomes the limitations of existing solutions by enabling secure, scalable, and contextually intelligent interactions across multiple tenants and communication channels, thus providing a robust foundation for enterprise-level AI applications. Reference symbol list 100 System 101 Backend Orchestration Layer 102 Variety of tenant-isolated vectors 103 Context Injection Module 104 Retrieval machine 105 dynamic prompt assembly module 106 Language Model Interface

Claims

A computer-implemented system for operating a multi-tenant, language-enabled artificial intelligence platform, comprising: a backend orchestration layer (101) configured to receive user input via a speech or text interface and resolve a tenant identifier associated with the user input; a plurality of tenant-isolated vector knowledge stores (102), each corresponding to a respective tenant and configured to store vector embeddings of tenant-specific data; a context injection module (103) configured to retrieve tenant-specific metadata, agent configuration, and conversation state prior to document retrieval and integrate them into a processing pipeline;a retrieval engine (104) configured to query only a corresponding, tenant-isolated vector knowledge store based on the tenant identifier in order to obtain relevant document vectors using similarity scoring and policy-based filtering; a dynamic prompt assembly module (105) configured to generate a prompt by combining retrieved document vectors, tenant-specific metadata, agent configuration, and user input; and a language model interface (106) configured to generate a response based on the prompt and deliver the response via a speech or text channel, thereby ensuring tenant-specific, context-aware, and secure interaction. System according to claim 1, wherein the client-isolated vector knowledge stores are automatically created and managed for each client when client-specific documents are ingested, and wherein each client-isolated vector knowledge store is physically and logically separated to prevent cross-client data access. System according to claim 1, wherein the context injection module is further configured to influence both the retrieval evaluation and the prompt composition by applying client-specific policies, compliance restrictions, and agent personality definitions prior to the execution of the retrieval process.

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