Your RAG works in the demo. Does it work in production?

Who it is for

Teams with a retrieval system or pilot — internal Q&A, support copilots, knowledge assistants — that hallucinates, leaks, drifts, or costs more than it should.

The problem it solves

RAG systems fail in the details: chunking that splits meaning, embeddings that drift from the corpus, access controls the retriever does not inherit, no eval suite so regressions are discovered by users. Each is fixable once measured.

What's included

• Corpus and pipeline review: chunking, embedding model, rerank, retrieval parameters — measured against eval data, not opinion
• Access-control and data-boundary audit: what can the retriever leak?
• Eval coverage assessment: does a regression suite exist, and does it cover real user questions?
• Cost and latency profile with rightsizing recommendations
• Fix-or-rebuild recommendation with reasoning

Deliverables

• Written findings report ranked by impact
• Eval coverage gap analysis
• Cost and latency profile
• Fix-or-rebuild recommendation with next-step options
• Read-out session with your team

Typical duration

One to two weeks, remote. Code and corpus access in the first days, measured findings and write-up after. See also our Enterprise RAG pattern for what “correct” looks like.

What happens afterward

Reviews convert into a RAG rebuild or retrofit, the Evals & AI Governance engagement — the 3–4 week path for a pilot that must become production-safe — or the Agent Deployment Accelerator when the RAG layer is meant to ground an agent.

Start free, then go deep

Our Agentic AI Readiness self-assessment is a free self-assessment you can finish in minutes. This engagement is the next level: we get inside your environment and produce findings your team can execute.