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Test the clinical behaviour that matters.
For teams that need realistic clinical scenarios and explicit assessment criteria, rather than relying on whether an answer sounds plausible.
When this is useful
You are building an evaluation set, reviewing AI-generated clinical feedback or finding that different reviewers judge the same response differently. Medicines workflows and clinical education are particular areas of experience.
How the work runs
- Map the behaviours and failure modes that need coverage.
- Author synthetic cases and reference criteria within the agreed clinical domain.
- Define severity, acceptable alternatives and how reviewers should handle uncertainty.
What your team receives
- A versioned case set with metadata and source references where relevant.
- A scoring rubric, error categories and reviewer instructions.
- A coverage map and recommendations for calibration and future expansion.
Scope and limits
Case count and complexity are agreed before quoting. A single-author set does not provide independent reviewer agreement; calibration, second review and empirical validation are additional work. Educational projects are screened for conflicts with SCRUB-in.
Start with your clinical AI question.
Send a short, non-confidential overview. I'll assess fit and discuss a written scope, fee and timing.