AI Assistant Using Your Business Knowledge Base — RAG on Your Documents

THE PROBLEM Q&A bots break down when knowledge lives in documents: a 100-page manual has no "questions" to match, it can't fit into a prompt, and generic chatbots hallucinate instead of admitting what they don't know.

THE SOLUTION A RAG (Retrieval-Augmented Generation) knowledge base: documents are split into meaningful chunks, embedded into a vector index, and the assistant answers from the right sections — by meaning, not keywords.

Any format as-is: PDF, DOCX, TXT, Markdown — 100+ pages is fine

Answers grounded in YOUR documents — it says "I don't have that information" rather than inventing

Source references — every answer shows which document and section it came from

Runs on your infrastructure — documents never leave your control

One command to re-index after updating documents — documented, no programmer needed

LIVE DEMO Ask it about my products, pricing, deployment and support — it answers from 6 real documents with sources: https://kb-assistant-demo.dvc-ai.workers.dev

The 'I don't have that information' line is the part most RAG builds skip, and it's the one that matters. How do you set the cutoff? On mine, a fixed similarity threshold broke once I filtered results by user role. Scores shifted and it refused questions it could answer.

A closer look inside my Clause Check template. Clause Check is a SaaS AI concept I designed to make reviewing and understanding contracts a little easier. Here’s a look at more of the landing page, from the UI components and interactions to the small details that bring the design together.