Contextual AI Routing Engine for Policy-Compliant Model Selection
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Solution Overview
Problem
Current systems lack the capability to dynamically and intelligently route user requests to the appropriate AI endpoint based on the content and context of the request, leading to inefficiencies and potential security breaches when personal and enterprise AI systems are used interchangeably.
Innovation Solution
A rules engine that integrates management rules, user preferences, and contextual analysis to evaluate requests and route them to the most suitable AI endpoint, utilizing a specialized routing AI Model for complex decisions and ensuring compliance with enterprise policies.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Adaptability or versatility
If users access multiple AI endpoints (personal and managed) interchangeably, then users can utilize diverse AI capabilities, but resource misallocation and unauthorized data access occur
Solution Approach 1:
The patent introduces a rules engine as an intermediary component that sits between users and multiple AI endpoints. This rules engine evaluates user requests against predefined management rules and dynamically routes requests to appropriate endpoints (personal or managed), preventing direct unauthorized access while maintaining versatile AI capability access.
2Ease of operation
If enterprise resources are used for personal requests, then users gain convenient access to AI tools, but computational power is wasted and costs increase
Solution Approach 1:
The system implements feedback mechanisms where the rules engine continuously monitors request characteristics, user profiles, and endpoint utilization. Based on this feedback, the engine dynamically adjusts routing decisions to direct personal requests to personal endpoints and enterprise requests to managed endpoints, optimizing computational resource allocation while maintaining ease of access.
3Ease of operation
If personal AI models process enterprise requests, then users can access AI functionality, but data security protocols are violated and sensitive information is exposed
Solution Approach 1:
The patent implements preliminary action by establishing management rules and routing logic before requests are processed. The rules engine pre-evaluates request characteristics, user authorization levels, and data sensitivity classifications, making routing decisions in advance to ensure enterprise requests with sensitive information are directed to secure managed endpoints before any processing occurs, preventing data security violations.
4Reliability
If a rules engine implements strict management rules, then data security and policy compliance are enforced, but system complexity increases
Solution Approach 1:
The patent applies segmentation by dividing the routing system into modular components: a rules engine, management rules module, user profile module, and endpoint registry. Each component handles specific aspects of request routing independently, making the system easier to manage and maintain despite the complexity of enforcing multiple management rules across diverse AI endpoints.
Data Source
AI summary
The invention provides a rules engine that manages user requests within an interface integrating multiple AI platforms and AI Models. Upon receiving a query, the engine assigns scores based on factors like management rules, user preferences, and contextual information. Determinative scores, such as those enforcing strict enterprise policies, can override others, leading the engine to block or reroute the query. If no score is determinative, the engine forwards the query and associated prompts to a specialized routing AI Model for contextual analysis. Based on this analysis, the rules engine directs the query to the most appropriate AI Model or defaults to the user-designated AI Model. This system balances user intent with rule enforcement, optimizing query processing across various AI platforms while ensuring compliance with enterprise guidelines.