AI Knowledge Hub

Designed to demonstrate how a knowledge assistant can help teams locate source-backed answers across approved internal material while retaining clear retrieval boundaries.

Teams often lose time searching across policy documents, product notes, and technical references, while a generic chatbot cannot show where an answer came from.

The concept pairs document ingestion and metadata with retrieval-augmented generation, then presents answers alongside their supporting source passages and controlled access context.

• Document ingestion with source and collection metadata
• Semantic search and conversational retrieval
• Inline citations that link answers back to source material
• Role-aware collections and workspace permissions
• Feedback capture for improving retrieval quality

• Ingestion pipeline for parsing, chunking, and embedding approved documents
• PostgreSQL for workspace metadata and access control
• Vector index for similarity search with metadata filtering
• API layer that retrieves context before invoking an LLM
• Evaluation-ready logging for retrieval and answer review