ON THIS PAGE (3 SECTIONS)
- ✓If retrieval can see documents the user cannot, the system is a data leak with a chat UI.
- ✓Grounding, citations, and eval gates belong in the release path — not as optional polish.
- ✓Use Vasiva’s RAG checklist and services pages when scoping a production retrieval system.
Enterprise RAG projects often optimize embedding quality while leaving authorization as an afterthought. In production, the first question is not “which vector database?” It is “which documents may this principal retrieve, under which policy, with what audit trail?”
Minimum bar before trusting answers
- Identity of the caller is known and mapped to document ACLs or row-level policies.
- Retrieval filters enforce those policies before generation — not only after.
- Answers cite sources the user is allowed to open.
- Low-confidence or conflicting retrieval escalates to a human or a refusal.
- Evaluation sets include permission-boundary cases, not only happy-path Q&A.
- Freshness and deletion propagate so revoked access cannot linger in indexes.
Where to go deeper on Vasiva.co
Use the Enterprise RAG production checklist and the Enterprise RAG service page for architecture scope. Related engineering notes: production RAG data freshness, RAG evaluation harness, and prompt injection defense.
Honest commercial framing
Vasiva builds permission-aware retrieval and production AI systems for organizations that need verification, not demo theater. We are based in Coimbatore, India, and deliver remotely. We do not invent customer references on this page. If you are scoping a RAG system for production, contact us with intent=ai-system and bring your identity model and corpus ownership map.
Panneer
Systems EngineerPanneer builds production AI data channels and evaluation harnesses at Vasiva.
