How AI teams put verified professionals to work on evaluation, ground truth and red-teaming.
Example programs · customer results coming soon
A clinical AI team needed to know how often its discharge summaries left out key medication changes.
Physicians reviewed sampled summaries against a shared rubric, three per item, with disagreements sent to a senior adjudicator.
A labeled error set grouped by failure type that the team used to retrain and to set release gates.
A legal-tech model sounded confident but cited the wrong rules for some jurisdictions.
Attorneys licensed in the relevant states wrote reference answers and graded model outputs clause by clause.
A jurisdiction-specific benchmark with expert reference answers and per-item agreement scores.
A security assistant could be talked into giving unsafe exploit guidance.
Certified penetration testers ran structured adversarial sessions and logged every successful bypass.
A ranked list of jailbreak patterns and a refusal-training dataset written by specialists.
Tell us your domain and model goals; we'll design the expert pipeline.