Domain-expert review for regulated knowledge assistants
Licensed attorneys, pharmacists, and CFAs evaluating AI outputs in three regulated verticals. 8,000+ evaluations. Error taxonomy grew from 12 to 47 categories from discovered failure modes alone.
Client Context & Operational Challenge
An enterprise software provider embedding generative AI into its knowledge management platform needed structured validation that AI-generated responses met professional accuracy standards for regulated industries. Off-the-shelf evaluation tools could not assess domain correctness in legal, pharmaceutical, and financial advisory contexts.
Execution & Governance Model
Recruited credentialed practitioners — licensed attorneys, registered pharmacists, and certified financial analysts — as domain evaluators. Built a custom evaluation interface presenting AI output alongside source documents for fidelity assessment. Evaluators scored on a five-axis rubric covering accuracy, completeness, citation integrity, reasoning coherence, and regulatory compliance.
Scale & Velocity Constraints
- Three regulated verticals each with distinct accuracy and compliance requirements
- AI outputs blending retrieval-augmented generation with free-form synthesis — requiring evaluators to assess both source fidelity and reasoning quality
- Evaluator pool required active practitioners with current professional credentials
- Bi-weekly evaluation sprints synchronized with the client engineering release cycle
- Granular error taxonomy distinguishing factual errors, hallucinations, citation failures, and reasoning gaps
What Was Delivered
Asset Outputs & Deliverables
- Processed over 8,000 domain-specific evaluations across three verticals within a 20-week engagement period. Error taxonomy expanded from 12 to 47 categories based on discovered failure patterns. Client engineering team reported direct alignment between evaluation findings and model improvement priorities. Framework retained for ongoing post-deployment monitoring.
Operational Footprint
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