AI writes more and more of your code. Cursor, Copilot, Claude — a prompt, and out comes a merge request. The pace is impressive. But the real question in production isn't "was it written fast?" — it's: can you show it's correct?
From it works to a signed report.
Now that a machine writes the code, the work shifts from writing to accountability.
This is a claim, not a measurement. The strike-through and underline are the same marking the renvooi service uses to show a change in law.
The bottleneck shifts
Over the past six months I've been shipping production code with AI agents. What stood out: the bottleneck shifts from writing to accountability. Does this AI-generated change actually work? What's tested, what isn't? And can you prove it — to a reviewer, a client, or a SOC 2 / ISO 42001 auditor?
That's exactly where teams get stuck. The code merges, but there's no audit trail: why is this safe to ship?
Treat it as evidence, not a promise
That's why I built Quoderat: an independent evidence audit of one risky or AI-generated GitLab merge request.
We run your own tests and lint, review the diff, and deliver a signed evidence report that states the limits plainly:
- Proven — backed by command output
- Tested only — passed, not formally verified
- Not checked — out of scope this round
- Residual risk — what your reviewer still has to decide
Not "we prove your code is safe" — instead: here's what we can and can't show, with a name underneath. Honesty is the product here.
Fixed price, read-only or on your own runner — your code stays yours.