AI output is reviewed by a Cirro consultant before it reaches your system, so most errors are caught before you ever see them. Where something does get through, it is treated as a Cirro defect and fixed at our cost. Accountability sits with us and does not shift to a tool, exactly as it would if a person had made the error by hand.

Why errors get caught

AI is good at producing plausible output quickly, which means it can be confidently wrong. The delivery method is built around that fact rather than around a hope that it will not happen.

• Nothing generated is applied directly to a production environment. Changes are reviewed, then applied deliberately.
• Requirements go back to you in structured form for correction before build starts, which catches misinterpretation at the cheapest possible point.
• Test cases are generated from your requirements rather than from the build, so the tests check what you asked for rather than what was built.
• You run user acceptance testing on the result. Your team is the final check, and that step is not skipped because AI was involved.

The honest position

No implementation method eliminates defects. Manual implementations produce them too, usually through fatigue, missed notes, or an assumption nobody wrote down. AI shifts the shape of the risk rather than removing it: fewer transcription and consistency errors, and a different failure mode where output looks right and is not. The review layer exists because of that second category.

What this means for you

You should hold Cirro to the same standard you would hold any consultancy. If something is wrong, it is our problem to fix, and pointing at a tool is not an answer we would accept from ourselves.

Next step

If you want visibility into where AI was used on your own project, read Can we see how AI was used on our project?

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