HITL AI Evaluation
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.
Define what “good” and “bad” outputs look like, before evaluation starts.
Recruit the right end users or credentialed domain experts to create or judge outputs.
Build a rubric and systematically score real outputs against it.
Turn scores into clear, defensible findings & recommendations.
Repeat across the AI’s entire lifecycle, not just as a final QA gate.
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.
Train or fine-tune your own model
Eg: A healthcare company fine-tuning a clinical documentation model
We’d Evaluate:
Domain accuracy, safety, and bias across patient populations
Build an agent or copilot on a foundation model
Eg: A customer service copilot built on GPT-4
We’d Evaluate:
Task success rate, hallucination rate, escalation handling
Deploy or integrate a third-party AI tool
Eg: A law firm using an AI search tool over its case files
We’d Evaluate:
Retrieval accuracy, output quality, edge-case robustness
Need to prove compliance before shipping
Eg: Any company facing legal or regulatory pressure to launch
We’d Evaluate:
Safety and policy assessment, regulatory documentation
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.
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.
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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REPORT 1
See exactly where automated metrics fall short and find out what it will take to safely deploy AI, avoid costly consequences, and mitigate unpredictable user behavior.
REPORT 2
See exactly where automated metrics fall short and find out what it will take to safely deploy AI, avoid costly consequences, and mitigate unpredictable user behavior.
REPORT 3
See exactly where automated metrics fall short and find out what it will take to safely deploy AI, avoid costly consequences, and mitigate unpredictable user behavior.