AI Production Audit + Quick Wins
5 days. We diagnose your AI feature, then ship the 1-3 fastest fixes we find.
Your feature is measurably better at the end of the week — not just diagnosed.
€1,500 — half upfront, half on delivery.
- Does your AI search actually find the right answers — or just the closest-looking ones?
- Is anyone measuring quality, or is "it feels okay" the only metric?
- How often does the AI make things up, and how do we stop it?
- How fast is it, how much is it costing you, and what happens if usage spikes?
- What breaks when OpenAI is down, a user spams the system, or someone tries to game it?
- A written diagnostic report (10-15 pages)
- A pull request with 1-3 quick-win fixes already implemented
- A clear plan for what's left, with effort and impact estimates
- A 60-minute walkthrough call to go through everything
Our quick-win guarantee.
Half upfront (€750), half on delivery. If we don't ship at least one working improvement to your AI feature in 5 days, you don't pay the second half. You keep the diagnostic report and any code we wrote.
A "quick win" is defined as a code change, delivered as a pull request, that demonstrably improves at least one of: response quality, response latency, error rate, cost per request, or operational visibility. We agree on the definition with you in writing before we start.
Kickoff call, access setup, agreed scope — and quick-win targets defined in writing
We dig in: testing your feature, running real queries, implementing quick-win fixes
You get the report + the pull request with implemented fixes
60-min walkthrough call: go through the report, Q&A, what to do next
- Not a full implementation (separate AI Feature Sprint package)
- Not a stack migration
- Not a workshop format for teams (see AI Strategy Sprint)
What if you find nothing to fix?
Extremely rare. In 90%+ of cases the eval setup is missing entirely; in the rest there are at least retrieval-quality wins. If we genuinely find nothing actionable, you don't pay the second half — you keep the report and any code.
Do we need to give you access to our systems?
Just enough to use your AI feature — usually a regular test account is fine. We sign an NDA before we start.
Can the audit lead to a sprint?
Yes, ~50% of audit clients book a follow-up sprint. The audit price is independent and not a sales call in disguise — you walk away with a usable document and implemented fixes either way.
Technical detail — for engineers
- Retrieval quality measurement: Recall@K, qualitative inspection of test queries
- Eval setup review: presence of golden datasets, RAGAS / Promptfoo / custom; CI integration if any
- Hallucination & grounding analysis: faithfulness vs. answer relevance, citation correctness
- Latency profiling: end-to-end and per-stage; streaming behaviour; caching coverage
- Cost analysis: per-query token cost, modeling at projected scale
- Failure modes: rate-limit handling, fallback strategy, model-outage behaviour, prompt-injection surface, PII leakage
- Stack we work with: OpenAI, Anthropic, pgvector, Qdrant, Cohere/BGE rerankers, RAGAS, Promptfoo, LangSmith