Eval directory
Evals for Layer Health
6 evaluation packs covering adversarial robustness, safety gates, workflow quality, and operator-level checks for Layer Health AI products.
About Layer Health
Layer Health is an AI company focused on clinical and healthcare applications, building tools that help medical teams triage patients, match clinical trials, and navigate complex care pathways more safely.
Available eval packs for Layer Health
6 packs ready to run.
Cdi Workflows
47 graded scenarios covering edge cases, failure modes, and quality checks.
Clinical Extraction Nlp Engine
65 graded scenarios covering edge cases, failure modes, and quality checks.
Data Ingestion Ehr Connectivity
8 graded scenarios covering edge cases, failure modes, and quality checks.
Document Parsing Ocr Normalization
54 graded scenarios covering edge cases, failure modes, and quality checks.
Registry Abstraction Workflows
51 graded scenarios covering edge cases, failure modes, and quality checks.
Revenue Cycle Management Coding Workflows
48 graded scenarios covering edge cases, failure modes, and quality checks.
Why eval Layer Health AI
Layer Health's AI features ship behind brand promises about accuracy, safety, and reliability. Buyers and integrators need to know those promises hold up under adversarial prompts, edge-case workflows, and the long tail of real customer inputs — not just the demo path.
The Corsac eval library for Layer Health measures four dimensions teams care about most when deploying medical & clinical ai agents:
- Adversarial robustness — does the agent resist prompt injection, jailbreaks, and social-engineering attempts?
- Workflow quality— does it complete the task buyers were shown in the demo, on inputs that don't look like the demo?
- Safety gates — does it escalate or refuse when it should, and only then?
- Operator quality — does it preserve analyst trust by surfacing the right context at the right time?
Every eval pack above is hand-authored against Layer Health's public product surface and runnable in Corsac with your own data.