RAG you can put in production — and prove.
Kynexa governs retrieval and context assembly so RAG answers respect role, intent and sensitivity with citations and end-to-end lineage on every response.
The problem you own
Relevance alone is not an authorization decision. A RAG application can retrieve sensitive chunks that match a question but are not appropriate for the user's role or purpose. Without policy at retrieval and context assembly, teams cannot consistently explain what was selected, denied, redacted or used to produce an answer.
How it works
Before candidate chunks reach the model, the policy engine filters, redacts or annotates them based on role, intent and sensitivity. Answers are grounded and lineage-aware; every response traces back to its governed sources. Embeddings and completions route through a provider-agnostic gateway and you bring your own vector store.
What you get
Policy-aware retrieval
Chunks can be allowed, denied, redacted, annotated or scoped before the model sees them.
Identity, role, and purpose controls
Context selection reflects who is asking, why they are asking and the sensitivity of each candidate chunk.
Explainable retrieval
Record which chunks were considered, which policy was applied and why context was included or excluded.
Grounded, lineage-aware answers
Responses can be traced to governed sources, with citations and retrieval traces.
Outcomes
RAG that supports enterprise security review with governed context selection, citations, and evidence of policy enforcement.
Governed answers, with citations.
Representative product UI.
See Kynexa govern your AI — in 30 minutes.
Bring a real use case. We'll set up governed retrieval, reasoning and audit on your stack.