Fix wrong answers, cost spikes, and release regressions in production LLM systems

What does the AI Production Audit include?

A fixed-scope baseline of quality, cost, latency, retrieval behavior, and regression risk. The handoff includes findings, a failure map, prioritized fixes, and a decision memo your engineering and leadership teams can use.

Why is our RAG not working in production?

Usually it is retrieval quality, chunking, embedding mismatch, ranking, missing evals, or production drift. We isolate the failure mode before recommending prompt, retrieval, model, or tooling changes.

How do you measure hallucination rate?

We define a groundedness and correctness rubric, build or improve a golden set, then run human review or LLM-as-judge where appropriate. The important part is repeatability: the same checks should catch future regressions.

Can you audit a vendor-built AI system?

Yes. We run second-opinion audits on vendor-built systems: baseline performance, identify root causes, and provide a recovery plan. If your team controls the code or configuration, we can also scope PR-level fixes.

Do you need production access or customer data?

No. We can operate on staging, redacted datasets, or sample conversations. Read-only logs and metrics are typically sufficient—and no customer data is used to train models.

What happens after the audit?

If the baseline shows clear ROI, we can scope an Optimization Sprint. If the fix is small, your team can use the roadmap directly. If ongoing drift is the main risk, a retainer may make sense.