AI Knowledge Assistant (RAG)
What It Does
Turn scattered PDFs, emails, contracts, and legacy docs into an AI that finds the answer in seconds — with sources cited. A self-hosted option keeps sensitive data in-house.
How It Works
01 — Document Ingester (PyMuPDF): reads PDFs, emails, and docs while preserving structure.
02 — Chunker: splits content into token-aware ~1000-token chunks with 100-token overlap.
03 — Embedder (Sentence Transformers): converts chunks into 384-dimension vectors.
04 — Vector Store (ChromaDB): stores vectors with cosine similarity and per-client isolation.
05 — Retrieval Agent: returns the top-K relevant chunks for each query.
06 — Answer Agent: answers from the retrieved chunks only, with mandatory citations.
07 — Document Generator: produces formatted .docx and .xlsx outputs.
Human-in-the-loop: the team flags errors, and conversations are logged for compliance.
Ask a question in plain language and get an answer grounded in your own documents, with citations.
- Ingests any folder, no cleanup
- Answers cite file and page
- Self-hosted option for privacy
- Generates SRS, RTM, gap analyses
- Multi-project isolation
- Auto-updates with new documents
Why AI Knowledge Assistant
Answers in seconds
Find the answer in about 5 seconds instead of 30+ minutes digging through documents.
Sources you can trust
Every answer is drawn from your documents and cited as [filename.pdf, page X], with no invented facts.
Scales across your archive
A single deployment has handled 173 PDFs, staying organized with multi-project isolation.
Private and low-cost
Roughly $0.001 per query in the cloud, or $0 fully self-hosted for sensitive data.