We route each item to calibrated domain experts and return structured labels, rationales, confidence, disagreement analysis, and QA-ready exports — not a forced majority vote in a spreadsheet.
One item goes to 3–4 experts. Each reviews independently. Disagreement is measured, not hidden — and we only adjudicate to a single label when the task genuinely requires one.
If a generic crowd label would be wrong, low-trust, or impossible, this is the work we do. We label the items where domain understanding actually changes the answer.
Benchmark creation, red-teaming, and calibration studies are advanced use cases of the same multi-expert engine — not separate products you have to navigate.
Every batch ships as files you can drop straight into a training or eval pipeline — with the QA evidence that makes the data defensible internally.
Reviewers are working subject-matter experts who would recognize the work as legitimate. We source on demand and qualify every annotator before production.
