You launch on a number. Not on a hunch.
Right now "is it good enough to ship" is a feeling in the room. I turn it into a measured score on your real cases, with the bad outputs caught by guardrails before a customer ever sees them. The launch decision stops being a gamble and starts being evidence.
Turning "it feels right" into evidence.
Reliability engineering for AI is the test harness and the monitoring that make a model's behavior visible, measurable, and catchable before it reaches a customer.
- Evals. A test set built from your real cases that scores accuracy, so you launch on a number, not a hunch.
- Guardrails. The runtime checks that block bad output - hallucinations, off-policy answers, unsafe actions - before they ship.
- Observability. Traces and metrics that show you what the system did and why, so a failure is diagnosable instead of a mystery.
- Audits. A written review of an AI-generated or vibe-coded system, flagging where it will break before your users find it.
The same question, in rooms where being wrong costs the most.
Any organization putting an AI answer in front of someone who will act on it needs the same thing: proof it is right often enough, and a net under the times it is not. The higher the stakes, the less "it seems to work" is allowed to be the answer.
The answer a person will act on
A support or triage assistant gives guidance that someone treats as authoritative. Scored evals plus guardrails are what let the team ship it knowing how often it is right and what it refuses to answer.
The model an auditor will question
A regulator or risk team asks "how do you know it works." Evals, traces, and a written reliability report turn that from a panic into a document already on the shelf.
The service citizens depend on
An agency or nonprofit puts an AI service in front of the public, where a wrong answer erodes trust fast. Observability and pre-launch audits catch the failure modes before they reach the people being served.
Treat your prompts like code: nothing ships until it passes the eval gate.
Most teams still judge an AI change by trying it a few times and feeling good about it. That is the same mistake software made before automated tests. The fix that matured over the last year is the eval gate - a graded test suite that runs on every prompt or model change and blocks the ones that regress.
Real cases, scored
A fixed set of inputs that mirror your actual traffic, each with a clear notion of a right answer - the dataset that makes "did this get worse" a measurable question instead of a hunch.
A model that judges output
Where there is no exact string to match, a separate model scores each answer against your rubric - LLM-as-judge - so subjective quality still gets a repeatable number.
Block the regression
Wire the suite into the change process. A new prompt or model that drops the score does not ship. You catch the regression before your users do, not after.
I build the eval set and the gate around your real cases, so quality is enforced automatically instead of checked by hand - a discipline most teams shipping AI for small operators still do not have. Related: why flaky AI is also a cost problem →
Fixed scope. Async. One payment after the audit.
- Scope and audit. You send the system and the failure you most fear. I return a fixed price and a plan within 24 hours, or a straight no.
- Build the eval set. Real cases, scored, so we both see the accuracy number and the failure modes.
- Add guardrails and observability. Runtime checks plus the traces that make every decision visible.
- Deliver the report. The number, the risks, the fixes, and a monitoring setup your team keeps running.
Before: you hope it works. After: you have the number.
Most AI features ship with no measurement at all - "it looked right in the demo." An eval set runs the feature against dozens of real and adversarial cases and gives you an accuracy number plus the exact inputs that break it, before a user ever sees them.
I build reliability tooling as a product, not a slide - including Runcap, which gates and verifies AI runs - and I audit AI-coded systems with a written report before any fix is paid for. See the builds →
One AI feature that quietly gives wrong answers to 5% of users can churn customers faster than any marketing wins them back. Catching it before launch protects revenue that a single bad-press week could erase.
Tell me what you are about to ship
Send me the AI system and the failure you most fear in front of users. Within 24 hours you get a free written teardown of it - what I would build, what it would take, and a fixed price - or a straight no.
Get my free teardown →