We build the full evaluation stack: trace-level instrumentation across every LLM call and retrieval event, golden datasets constructed with your clinical SMEs, domain-specific rubrics that capture the actual clinical logic your AI is supposed to follow, and a four-bucket failure taxonomy that routes each type of failure, whether fabrication, documentation gap, wrong abstention, or generation coding error, to the upstream layer that can actually fix it. Three evaluator types run in concert: rule-based scorers for deterministic checks, LLM-judge scorers for ambiguous clinical reasoning, and human-in-the-loop annotation that validates both and continuously enriches the golden dataset. Eval pipelines wire into CI/CD so every prompt change is scored before it ships, and model cards are produced as deployment artifacts.