Eval suite · LLM-cost guardrails · Regression gate
Prove every AI change is an improvement, not churn
Your AI feature works in the demo. Does it stay correct — and in budget — in production? In two weeks, CodeWheel builds the eval suite plus cost and regression guardrails around your LLM feature and commits them to your repo, so a prompt tweak, a model swap, or a traffic spike can't silently break quality or blow your bill. Built on open-source PromptWheel.
The shape
2 Weeks
Fixed-scope, fixed-price engagement
$8k–$15k
Set after the teardown — no hourly, no retainer surprise
In Your Repo
Working code committed to your codebase, not slideware
Fixed-scope pricing with no surprise invoices.
The problem
You probably recognize this
AI features ship churn you can't tell apart from improvement, and the eval and cost gaps usually surface right after the first GenAI hire goes looking.
"Works in the demo" ≠ works on real inputs
AI-assisted features ship measurably more defects. Without evals, you can't tell whether a change made the feature better or worse — prompt and model changes ship on vibes.
No oracle for "better or worse"
There's no number that says a prompt tweak improved quality without regressing something else. So you can't tell shipped improvement from shipped churn.
Open-ended spend
One runaway loop or a power user and the bill spikes. The classic shape: a per-run cap exists in the config and is passed by no call site — so nothing actually bounds it.
Silent model swaps
Swapping GPT→Claude or Haiku→Sonnet silently changes behavior and cost. The eval and cost gaps usually surface right after the first GenAI hire — when someone finally goes looking.
What you get
Concrete, committed to your repo
Eval suite
Golden cases for your feature plus LLM-vs-ground-truth scoring (and judge/refutation where outputs are open-ended), runnable in CI. You can finally answer "is it working?" with a number.
Cost guardrails
Per-request and per-run spend caps, a pre-run "this will cost ~$N" estimate, per-user quotas, and a spend signal. Turns an open-ended bill into a bounded one.
Regression gate
Every PR — and every prompt or model change — runs the evals plus a PASS-to-PASS and outcome check. A change that worsens quality or cost fails before it merges. Built on the open-source PromptWheel outcome gate.
Reliability report
Your current failure modes, what's now guarded, and the top 3 risks to watch — so the team knows exactly where the edges still are.
How it works
The two-week shape
Start with a free teardown, instrument what exists, then guard and gate it. 2-week default; 3 weeks for multi-feature or messy infrastructure.
Week 0 — Free Reliability 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 whether or not we work together.
Week 1 — Instrument
Golden cases, the eval harness, and a cost map. We make the current behavior measurable before changing anything.
Week 2 — Guard, gate & report
Cost guardrails, the CI regression gate, the reliability report, and a handoff. 2-week default; 3 weeks for multi-feature or messy infrastructure.
Fit
Who this is for
Who it's for
Product teams (~5–50 engineers, seed–Series B) that have shipped or are shipping an LLM feature and have at least one of: no real evals, unpredictable/open-ended model spend, or the "we changed the prompt and something broke and nobody noticed" problem.
Not a fit if
You haven't shipped an AI feature yet (nothing to measure), or you want strategy slideware. This is working code committed to your repo.
FAQ
Common questions
What exactly do I end up with?
An eval suite, cost guardrails, and a regression gate committed to your repo, plus a reliability report. Everything runs in your CI on every PR — it's working code you own, not a document.
What does it cost?
A fixed $8k–$15k, set after the free teardown so the number reflects your actual scope. No hourly billing and no open-ended retainer surprise. An optional Reliability Retainer can keep the guardrails current as you keep shipping.
How long does it take?
Two weeks by default: Week 1 to instrument (golden cases, eval harness, cost map), Week 2 to guard, gate, and report. Multi-feature or messy infrastructure can extend it to three weeks.
What is PromptWheel and why is it under the hood?
PromptWheel is the trustworthy per-turn reward for AI coding loops — the outcome gate that proves a change moved a metric without regressing another. It's the signal inside a loop, not a driver. It's open source (MIT), zero-dependency, and also a Claude Code plugin. The sprint builds your guardrails on top of it, so nothing here is locked to me.
What is the free teardown, exactly?
A 30–45 minute live look at your AI feature where I name the top 2–3 reliability and cost holes. No obligation, and you keep the findings. It also lets us both confirm the sprint is a fit before any money changes hands.
Why you?
Ex-Tesla engineer who builds verification and eval harnesses for AI systems. My OSS PromptWheel is exactly this thesis — prove every change moved a metric without regressing another. Building cost and eval guardrails for AI features is the work I do.
Start with a free teardown
A 30-minute live review of your AI feature. I'll name the top 2–3 reliability and cost holes — no obligation, and you keep the findings. If the sprint isn't the right fit, I'll tell you.
Contact
Email: matt@codewheel.ai
Reply "teardown" and I'll send times. Based in the Bay Area; happy to meet virtually or in person if you're nearby.
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