Expert human data, verified at source.
What we learn running the verified human side of AI training — published, with receipts.
The 2026 buyer's framework: when you actually need verified experts, the six criteria that separate vendors, and the red flags that should end a conversation early.
A definition worth being precise about: verification at source confirms facts where they live — the registry, the employer, the system of record — instead of trusting documents supplied by the person being vetted.
A working definition: provenance is the auditable link between a unit of training data and the verified person who produced it. What that means in practice, and why per-unit is the only granularity that counts.
Clinical evaluation is the sharpest case for verified expertise: the errors that matter are exactly the ones a confident non-clinician cannot see.
Every human-data vendor now says 'verified experts.' A working checklist for finding out what that actually means — ten questions, and the answers that should worry you.
Most AI-training workforces are vetted with a resume and a webcam. Here is what it actually takes to establish who did the work — identity, employer, role, and seniority, against systems of record.