Secure Enterprise RAG / Knowledge Intelligence Platform
RAG that respects permissions, cites its sources, and doesn’t get poisoned by a bad document.
Role: Lead architect — retrieval pipeline, security model and platform design.
The problem
A basic RAG chatbot will happily retrieve the wrong document, ignore permissions, surface poisoned content, or answer confidently with no real source behind it. Enterprise knowledge deserves stronger guarantees — this platform enforces access control at the index and query level, verifies citations, and evaluates retrieval and generation quality continuously.
Business Context
A basic RAG chatbot inherits every permission mistake in the underlying document store and adds new failure modes of its own — poisoned content, hallucinated citations. Enterprise knowledge needed retrieval that respected the same access boundaries as the source documents, not a layer that treated every user as equally trusted.
Architecture
Key capabilities
Permission-aware retrieval enforced at the index and query level
Hybrid keyword + vector retrieval with reranking for precision
Citation verification — answers are checked against their sources
Controlled detection for RAG-poisoning and retrieval-injection patterns
Tenant isolation with full ingestion and search observability
Security & Risk
A RAG system inherits every permission mistake in the underlying document store, plus new ones of its own — poisoned content, hallucinated citations, retrieval that ignores who's asking. This platform enforces access control at the index and query level and verifies citations before an answer is trusted.
Outcome
Enterprise knowledge that respects the same access boundaries as the source documents, with retrieval and generation quality evaluated continuously rather than assumed.
Challenges
Enforcing permission-aware retrieval at the index and query level, rather than filtering results after the fact, required tenant isolation strict enough that a query literally couldn't surface a document outside the requester's access — combined with citation verification so an answer's claimed sources were checked rather than assumed, and controlled detection for RAG-poisoning and retrieval-injection patterns.
Lessons Learned
A RAG system's biggest risk usually isn't the model, it's the retrieval layer trusting its inputs. Access control belongs at the index and query level, and citations need to be verified rather than trusted — a confidently-cited wrong answer is worse than an honest “I don't know.”
Architecture pillars
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Technology
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