Standard engagement
AI Reliability Audit
Find out if your AI feature actually works — in two weeks.
You shipped an AI feature and now you have no idea if it works in production. Two weeks: build evals, measure hallucination rate, latency, cost-per-request, and ship a regression suite that fails CI when quality drops. You leave knowing exactly where the model is wrong, how often, and how much it costs you.
from $4,500
2 weeksLLM evalsOpenAIAnthropicLangSmithBraintrustCI gates
What ships during the engagement.
Eval dataset (50–200 cases) sourced from your production logs or hand-curated golden set
Eval runner wired into CI — fails PRs that drop quality below threshold
Hallucination + accuracy + latency + cost dashboards
What you walk away with.
- Hallucination rate measured on a real dataset
- Latency + cost-per-request profile across providers
- Regression test suite that runs in CI
“They scoped, shipped, and operated our RAG pipeline in twelve days. Citation accuracy on our eval set landed at 92%, and ongoing tuning costs us less than a Slack seat.”
- What if we have no eval dataset?
- We build one with you. Two days of source-mining production logs and curating a golden set is the first phase of every audit.
- Which models do you cover?
- OpenAI, Anthropic, Google, open-weights via vLLM/Together/Replicate. We test cost-vs-quality across at least 3 providers as part of the audit.
Want to scope AI Reliability Audit?
A short call to confirm fit and timeline.