AI quality assurance that runs before anyone else reads it.

Generated work fails differently from human work. It is fluent, well formatted and confident, which is exactly why it passes the glance test that most review processes rely on. The defects sit in assumptions rather than syntax: a function that handles the happy path and silently returns nothing for the rest, a statistic with an invisible source, a screenshot that lost the metadata proving where it came from.

Traditional QA assumes a fixed requirement and a deterministic intent. Generative pipelines have neither. What they do have is repeatable failure modes, and those can be tested.

A gate worth relying on has four layers, and each one answers a different question.

Offline checks can prove the absence of known defects. They cannot prove the presence of quality: whether the argument holds, whether the tone fits the audience, whether the liability sits somewhere nobody looked. Pretending otherwise is how verification tools become a rubber stamp.

So the report states what ran, what it found and what it could not see. When the stakes are high enough that the gap matters, a specialist reads the work, records the changes they made and signs the verdict. Both artifacts attach to the deliverable.