How Human QAQC Makes AI Engineering Deliverables Client-Ready

Most clients love the speed of AI—until they realize a robot handled their technical work. That’s why Singularity built a two-level QAQC system to bridge the gap between automation and trust.

The Two-Layer Check That Builds Confidence

Singularity’s workflow is AI-first, but no deliverable gets issued without passing through two stages of human review. Level 1 is a self-check: the person who ran the calculation marks up the file, double-checks assumptions, and ensures it makes sense.

Level 2 is a second-person review. That reviewer combs through the work again, flags missed logic or assumptions, and adds markup and commentary. Every version is logged with timestamps and initials in a structured folder—so there’s no confusion over who checked what.

These steps give clients what they really want: confidence that the AI didn’t run wild, and that real people took accountability.

Why It Matters in a Pure-AI Workflow

While AI gets it right most of the time, it doesn’t catch edge cases, unclear data, or poor inputs. More importantly, many clients still don’t fully trust AI. They want the assurance that someone reviewed the work manually—even if AI did the heavy lifting.

QAQC doesn’t slow down the process. It makes AI work credible. In fact, it’s what turns a raw calculation into something ready for client use. For companies delivering engineering or technical work through AI, this kind of structured human review is essential.

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