Generative AI Governed Search for Structured Data
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Solution Overview
Problem
Businesses face inefficiencies and data management challenges due to the surge of both structured and unstructured data, leading to data chaos and information management nightmares, with current solutions lacking effective governance, privacy, and data integrity in the face of increasing data volumes.
Innovation Solution
A system and method utilizing generative artificial intelligence (AI) and large language learning models to parse and search through structured and unstructured datasets, incorporating a governed search component, data storage, data identification, model integration, governance, autonomous agents, advanced workflows, security, and records management to efficiently organize, store, and access data while maintaining privacy and integrity.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Loss of information
If generative AI and large language models are used to parse and search through structured and unstructured datasets, then data comprehension and search capability are improved, but computational resources and processing time are increased
Solution Approach 1:
The system performs preliminary data classification, categorization, and parsing before the actual search operation. Data is pre-processed and organized into structured formats with metadata tags, enabling faster retrieval and reducing the computational burden during search operations. This preliminary organization allows the generative AI to work with pre-categorized data rather than raw data, significantly reducing processing time while maintaining comprehension quality.
2Loss of information
If generative AI and large language models are used to generate detailed content from datasets, then content quality and relevance are improved, but computational resources and storage requirements are increased
Solution Approach 1:
The system creates compressed representations and embeddings of the original data rather than storing all raw data. Generative AI models work with these condensed vector representations and key feature extracts, which capture the essential information needed for content generation while occupying minimal storage space. The system retrieves and generates content based on these compressed forms, maintaining quality while dramatically reducing storage requirements.
3Reliability
If comprehensive data governance and security measures are implemented, then data integrity and privacy are improved, but system complexity and operational overhead are increased
Solution Approach 1:
The system implements automated governance mechanisms that self-regulate data access, classification, and security compliance without requiring extensive manual intervention. AI-driven policies automatically classify data sensitivity levels, manage access permissions, and enforce governance rules based on predefined criteria. This self-service approach maintains high data integrity and privacy standards while reducing the operational overhead and complexity associated with manual governance processes.
Data Source
AI summary
A system and method for providing governed search capabilities using generative artificial intelligence (AI) and large language learning models to permit users to parse and search through and to generate detailed content from both structured and unstructured datasets.