Your AI-generated pipelines pass every test. That's not the same as working.

A fixed-scope audit that tells you which of your event-driven flows are actually moving data — and which have been quietly dead for months.

Tell me about your system →

THE FAILURE MODE

The seam is the problem

When AI writes your integration code — the handlers, the subscriptions, the producers and their consumers, the fallback paths — every individual piece can be correct and reviewed and still leave you with a system that isn't doing anything.

The seam is the problem. Nobody, human or model, can verify runtime data flow by reading static code. So the failure doesn't crash. It produces a convincing artifact of working: data in the right table, in the right format, on the right schedule, that means nothing.

You don't find out. That's the whole problem.

WHY YOUR EXISTING CHECKS DON'T CATCH IT

Three things you already trust, and why none of them see this

Your tests

If the same model wrote the handler and the handler's test, the test verifies what the code does — not what it was supposed to do. A handler that returns a correctly-formatted meaningless number passes a test that checks the format.

Your monitoring

Advisory monitoring checks whether code exists and endpoints respond. Not whether data flows.

Your dashboards

Green. Correctly green. The data really is fresh. It's fresh and fake at the same time.

HOW I KNOW

Since 2023 I've been building the plumbing between AI models and the things that use it — two complete builds, 1.1 million lines of production code as measured by git log.

I found four of these failures in my own system. Behind eighteen thousand tests, 419 automated checks, eighteen architectural rules, and a health endpoint that said HEALTHY.

One of them had been writing plausible records every five minutes for two months — seventeen thousand of them, every value different from every other value — while the handler that was supposed to write them never fired once. If you'd asked me whether it was running, I'd have shown you the table as proof. I'd have been wrong, confidently, with evidence.

The first version of that system was human-guided. It had none of these. The rebuild was AI-generated end to end, more sophisticated, more thoroughly tested. It had four.

I wrote a book about it — Silent Wiring. This audit is that method, run against your system instead of mine.

WHAT THE AUDIT IS

Fixed scope. Fixed fee. Two to four weeks. Not open-ended consulting.

  1. Your integration map

    Every event-driven flow in the system: its handler, its subscribers, its consumers, its fallback paths. Most teams have never seen this written down.

  2. A verdict per flow

    Live · degraded · counterfeit · monotonous. Named, with the evidence attached.

  3. Working checks, installed

    The behavioural checks that catch each failure type, running in your CI when I leave. Not a slide deck of recommendations.

  4. A written report your team can act on without me

    Prioritised, with the reasoning, so your engineers can argue with it.

WHO THIS IS FOR

A fit if

  • you're shipping AI-assisted code into a production system with real event-driven or pipeline architecture
  • you have monitoring that's green and you're not fully sure you believe it
  • someone on your team has said "I think that's running."

Not a fit if

  • you want a general code review, a linter, or a security audit — different problems, different people
  • your system is a pre-production prototype
  • you want an open-ended engagement with someone embedded in the team. This is a fixed piece of work with a defined end.

THE ASK

Tell me what your system does and I'll tell you within one business day whether an audit would find anything.

If the answer is probably not, I'll say so — that's a cheaper outcome for both of us than finding out in week three.

Include your name, your company, and one paragraph on the system — what it processes, roughly how many event-driven flows, and what your monitoring currently tells you.

Describe your system →