/01
The problem
Founders want product answers grounded in their own data. A general model without retrieval invents details and erodes user trust.
- Hallucinated answers
- No citations
- Stale knowledge
- Unbounded cost
Solution / AI
Document Search with RAG
Make a SaaS product answer questions from its own documents instead of guessing.
/ Outcome
Launch outcome.
A retrieval-grounded answer engine with citations, evals, cost ceilings, and admin controls.
/ System
/02
The implementation
We model the corpus, build a retrieval pipeline, ship the answer surface, and wire evals + observability before launch.
- Document chunking
- Embeddings + vector store
- Tool-call retrieval
- Citations in UI
- Eval set + cost dashboard
/03
Deliverables
A live RAG endpoint integrated into the product, an admin surface for the corpus, and a stabilization plan for ongoing accuracy.
- Indexing pipeline
- Answer endpoint
- Admin uploads
- Eval set
- Runbook
/ FAQ
Questions founders ask.
Do we need fine-tuning for this?
Usually not. Retrieval, better context, and a small eval set solve the first version more reliably.
How do you handle private documents?
We scope retrieval per tenant, log access, and gate the answer surface behind your existing auth.