Human Data & Benchmarking

Human-Centered Ground Truth for High-Stakes AI

Benchmark, evaluate, and improve your models using clinician-generated data and expert-led evaluation frameworks.

Your models need the right balance of safety and personality to keep users delighted.

mpathic partners with:

AI builders deploying conversational agents
Safety & Responsible AI teams
ML teams needing high-quality evaluation datasets

To ensure our systems meet the highest standards of safety and reliability, we benchmark model outputs against expert human judgments. This means rigorously comparing AI-labeled outcomes to assessments made by trained clinicians. This process helps validate that our models not only perform well, but do so in ways that are clinically meaningful, ethically sound, and aligned with real-world expectations.

What Sets Us Apart

A Thought Partner in Responsible AI
We support ML and product teams by offering model auditing, performance benchmarking, and ethics-forward design feedback. Our human evaluation infrastructure helps teams identify where their models succeed—and where they need to do better.

Clinician-Powered, Not Just Reviewed
Our expert clinicians are trained to script nuanced conversations, annotate for clinical intent, and evaluate system safety using validated frameworks. We also develop rubrics that center therapeutic alliance markers, and high-risk communication rubrics.

Proven Methods, Published Results
We’ve spent over a decade working at the intersection of behavioral health, AI, and safety science. Our datasets and models have been validated in real-world settings and cited in research on fidelity, therapeutic effectiveness, and LLM risk assessment.

Peer-reviewed publications

What We Provide

Expert-curated synthetic conversations across diverse clinical scenarios

Annotation and scoring aligned with evidence-based coding systems

Performance metrics including F1, precision, recall, and absolute agreement with human gold standards

Safety review workflows tailored to sensitive content domains

Guidance for building safe, trustworthy AI tools from the ground up