HITL AI Evaluation
We use actual doctors, lawyers, financial analysts, and other credentialed specialists to evaluate complex domain reasoning, not generic crowd workers.
We go beyond “is the model mathematically correct?” to ask, “Will employees blindly trust it?” We measure Automation Bias using validated UX and psychometric frameworks.
We test against hard regulatory frameworks (OWASP, NIST, WCAG) to deliver the exact paperwork your legal and compliance teams need to launch safely.
We don’t guess at good. We define it precisely for your use case, then validate it with domain experts against a calibrated, defensible rubric and industry standards.
Human-in-the-Loop (HITL) AI Evaluation means trained people, not just automated AI judges, systematically evaluate whether an AI system is accurate, safe, fair, and actually useful.
Research methodology expertise. We design the rubrics, protocols, and calibration processes that make human judgment consistent and defensible.
Access to the right people. A well-profiled, vetted panel means we recruit precisely the right participants or domain experts for a given evaluation.
Deep AI-specific expertise. We understand how these systems actually work, so we test what matters instead of running a generic checklist.
AI systems can score well on automated benchmarks and still fail in the real world: biased outputs, confidently wrong answers, missing context a human would catch immediately.
AI systems can score well on automated benchmarks and still fail in the real world: biased outputs, confidently wrong answers, missing context a human would catch immediately.
Before any model is tested, Planning defines what “good” looks like: the tasks, the users, the rubric, and the regulatory scope.
Golden datasets get built and candidate models get baselined against ground truth, so every later stage has something real to measure against.
Benchmarking, bias, safety, and localization checks run early and often, measured by credentialed professionals rather than the model’s own builders.
In this three-part series, you’ll follow the same 372 AI professionals from a single question, “is this working?”, to the uncomfortable answer: nobody actually knows, and almost no one owns finding out.
The AI Evaluation Gap
Who’s Actually Testing the AI?
The Risks Teams Worry About and Rarely Measure
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