Taxonomy, Rubric, & Policy Design

Development by Advanced AI Experts

We design the evaluation frameworks that define quality, safety, and performance in AI systems — so your teams can measure, align, and improve models with confidence.

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You Can’t Measure What You Haven’t Defined

Without clearly defined criteria and structured behavior categories, evaluations become inconsistent, safety judgments vary across reviewers, and metrics lack meaning.

mpathic’s Rubrics and taxonomies are the foundation of trustworthy AI systems.

Rubric Development to define evaluation criteria

Multi-level scoring frameworks
Pass/fail thresholds
Safety severity scales
Domain-specific quality standards

Policy-aligned evaluation criteria
Structured annotation guidelines
Calibration frameworks for human reviewers

Taxonomy development to classify model behaviors

Risk category hierarchies
Failure mode classification systems
Behavioral typologies

Safety incident labeling systems
Governance-ready reporting categories
Multi-level tagging schemas

Powered by the largest pool of safety experts

We work with thousands of top psychiatrists, doctors, clinicians and other safety experts to red team and evaluate models in ways that reflect real-world use, real users, and real risk.