Eval directory
Evals for Abridge
8 evaluation packs covering adversarial robustness, safety gates, workflow quality, and operator-level checks for Abridge AI products.
About Abridge
Abridge builds purpose-built AI that transforms healthcare conversations into insights. Its platform supports clinical documentation, revenue-cycle documentation, and nursing workflows.
Industry
Healthcare AI / Clinical Documentation
Website
www.abridge.comHow complete this published benchmark library is across datasets, metrics, rubrics, use-case maps, and pack context. This is library coverage, not an agent performance score.
Test datasets
8/8 packs
Scoring metrics
8/8 packs
Judge rubrics
8/8 packs
Use-case maps
0/8 packs
Pack context
8/8 packs
Available eval packs for Abridge
8 packs ready to run.
Audio Capture Ingestion
49 graded scenarios covering edge cases, failure modes, and quality checks.
Clinical Note Generation Structuring
58 graded scenarios covering edge cases, failure modes, and quality checks.
Connectivity Resilience Audio Upload Integrity
56 graded scenarios covering edge cases, failure modes, and quality checks.
Encounter Session Lifecycle Management
62 graded scenarios covering edge cases, failure modes, and quality checks.
Linked Evidence Source Audio Traceability
52 graded scenarios covering edge cases, failure modes, and quality checks.
Long Duration Device Resource Constraints
46 graded scenarios covering edge cases, failure modes, and quality checks.
Session Interruption Crash Recovery
User Interrupts59 graded scenarios covering edge cases, failure modes, and quality checks.
Speech Recognition Speaker Diarization
78 graded scenarios covering edge cases, failure modes, and quality checks.
Why eval Abridge AI
Abridge'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 Abridge 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 Abridge's public product surface and runnable in Corsac with your own data.