RAG systems that answer with citations from your own data.
Your team's collective knowledge — docs, wikis, manuals, past tickets, contracts — turned into an AI assistant that answers questions accurately and cites the source. Vector DB setup, smart chunking, re-ranking, evals, and a feedback loop that keeps improving accuracy.
When to hire us
- Your team can't find answers in your own knowledge base
- You want customers to self-serve from your docs (not browse menus)
- Generic AI gives wrong answers because it doesn't know your business
- You need an AI that cites sources, not hallucinates
The capabilities, spelled out.
Data ingestion & chunking
We connect to your sources (Notion, Confluence, Drive, Sharepoint, S3, databases) and chunk content with strategies that preserve meaning.
Vector + hybrid search
Vector embeddings + BM25 keyword + re-ranking. Better recall than vector alone, more precise than keyword alone.
Source-cited answers
Every answer comes with citations to the source documents. No hallucinations passed off as facts.
Sync & freshness
Auto-sync on document updates. Index always reflects current state, not last quarter's docs.
Access control
Respects your existing permissions — users see only what they can see in the source system. No data leaks.
Evals & feedback
Eval suite measures accuracy on your specific Q&A. User thumbs-up/down feeds back into improvements.
Our default stack.
We'll pick the right tools for your project — but if you don't care, this is what we usually reach for.
Outcomes, not just hours.
- Team finds answers in seconds instead of hunting through Confluence
- Customer support deflection rises 30–60% on documentation-answerable questions
- Answers are traceable — every claim cites a source you can verify
- Knowledge stays fresh — no out-of-date answers because docs changed
Let's scope your turn your docs into an answerable ai.
Tell us what you have in mind — we'll come back with a clear plan, timeline, and quote.