Generative AI Orchestration Platform for Privacy and Interoperability
Find Innovative SolutionsGenerate Solutions
Solution Overview
Problem
Existing generative AI platforms face issues such as lack of privacy protection, limited recent knowledge, incomplete knowledge domains, susceptibility to hallucination, incompatibility with proprietary information, interoperability challenges across different models, and inadequate legal, privacy, and ethical/bias checks, which hinder their effectiveness in corporate environments.
Innovation Solution
An interoperable generative AI orchestration platform that provides role-based access control, maximum discoverability of content, and democratization of development, enabling secure, accurate, and efficient use of generative AI systems by integrating role-based access control, a common knowledge base, and interoperability with various AI models, while ensuring privacy, accuracy, and compliance with ethical and legal standards.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Productivity
If existing generative AI platforms are used to provide direct answers, then user productivity is improved, but privacy protection is compromised due to lack of access control
Solution Approach 1:
An orchestration layer is introduced as an intermediary between users and multiple AI models. This layer manages authentication, authorization, and access control policies, allowing productive AI interactions while protecting privacy through role-based access control and policy enforcement.
Solution Approach 2:
The system segments AI models into different categories (e.g., public, private, enterprise) with distinct access policies. Each model can have its own security requirements and access controls, enabling privacy protection for sensitive models while maintaining productivity through appropriate model selection.
2Ease of operation
If existing generative AI platforms are used, then answer generation is enabled, but interoperability across different models is challenged
Solution Approach 1:
The orchestration layer provides universal interfaces and standardized interaction protocols that work across multiple AI models from different providers. This enables easy answer generation while maintaining interoperability through a unified access mechanism that can adapt to various model types and providers.
3Loss of information
If existing generative AI platforms are deployed, then knowledge access is improved, but hallucination susceptibility increases
Solution Approach 1:
The system implements feedback mechanisms where AI responses are evaluated against ground truth data, usage patterns, and user corrections. This feedback loop continuously improves response accuracy and reduces hallucinations while maintaining broad knowledge access through multiple models.
Solution Approach 2:
Multiple AI models are combined in the orchestration layer, allowing cross-validation of responses. When multiple models agree on an answer, confidence increases and hallucination risk decreases. Diverse model ensembles provide broader knowledge access while improving reliability through consensus.
4Productivity
If existing generative AI platforms are used, then development speed is improved, but ethical and legal compliance is inadequate
Solution Approach 1:
Ethical and legal compliance checks are performed preliminarily through policy configuration and model selection before actual AI interactions. Access control policies, usage restrictions, and compliance rules are established in advance, enabling fast development while ensuring ethical and legal adherence from the outset.
Data Source
AI summary
Aspects of the subject disclosure may include, for example, obtaining one or more information items or documents, checking the one or more information items or documents for determined sensitivity and determining role-based access for the one or more information items or documents, based on the checking and the determining, performing a preliminary analysis of the one or more information items or documents, wherein the preliminary analysis involves one or more pre-processing procedures, one or more chunking procedures, one or more embedding procedures, one or more customization procedures for particular use cases, or a combination thereof, and storing results of the preliminary analysis in a vector knowledge base for training one or more generative artificial intelligence (AI) large language models (LLMs). Other embodiments are disclosed.