Evidence for AI-generated code: from 'it works' to a signed report

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?

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.