How We Built an Automated QA System for AI Deliverables

Quality assurance for AI deliverables is a harder problem than QA for typical software work. A pull request either passes its tests or it does not; a fine-tuning run, a labeled dataset, or a research writeup requires judgment about correctness, completeness, and reasoning quality that a simple pass/fail check cannot capture.

Every deliverable passes through a layered pipeline before it ever reaches a client for approval. Automated checks handle what can be objectively verified — code correctness, test coverage, output-format compliance, adherence to the stated task requirements. These catch the majority of issues cheaply and immediately.

What automated checks cannot fully capture — reasoning quality, subtle correctness issues in a research writeup, whether a labeling decision reflects real domain judgment — routes to anonymous peer review from other high-scoring experts in the same domain. This catches the class of error that looks fine on the surface but would not survive scrutiny from someone who actually knows the field.

We deliberately did not try to automate away the final judgment call. Client approval is always the last gate before payment releases from escrow — automated scoring and peer review exist to make that final review fast and well-informed, not to replace it. The goal was never a fully automated black box; it was giving both experts and clients a shared, transparent quality bar before a deliverable is ever on the line.