The AI Reliability Sprint
Your AI feature works in the demo. Does it stay correct — and in budget — in production?
A 2–3 week, fixed-scope, fixed-price engagement: I build the eval suite, cost guardrails, and regression gate around your LLM feature, so a prompt tweak, a model swap, or a traffic spike can't silently break quality or blow your bill.
What the sprint ships — committed to your repo
How it works
Fixed $8k–$15k, set after the teardown — no hourly, no open-ended retainer surprise.
Week 0 — free teardown
A 30–45 minute live review of your AI feature. I name the top 2–3 reliability and cost holes. No obligation; you keep the findings.
Week 1 — instrument
Golden cases, the eval harness, and a cost map of your feature.
Week 2 — guard, gate, hand off
Guardrails, the CI gate, and the reliability report — committed to your repo, not a slide deck.
Built on published, verifiable work
The sprint's regression gate is built on PromptWheel — my open-source referee for AI coding loops: it re-proves every "win" from the source edits alone and returns GAMED when the green came from moving the goalposts. And the approach comes from data, not vibes: I studied ~2,300 merged agent PRs to measure how often tests actually get gamed — and who is actually checking.
Why engineering teams choose CodeWheel
Architecture, code, and quality from one person
Architecture + implementation
You don't get a slide deck handed to juniors. I design the system and ship the code.
Verification built in
Evals, guardrails, and regression gates ship with every feature — the referee is part of the build, not an afterthought.
Direct access
No account managers. You DM the person making the decisions and the fixes.
Other engineering work
Not everything is a sprint. Fifteen years of production engineering across these areas — each with its own page: