Eval directory
Evals for Innovaccer
6 evaluation packs covering adversarial robustness, safety gates, workflow quality, and operator-level checks for Innovaccer AI products.
About Innovaccer
Innovaccer provides the Innovaccer Health Cloud, a healthcare intelligence platform that activates fragmented healthcare data to support connected digital experiences and care delivery.
How 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
6/6 packs
Scoring metrics
6/6 packs
Judge rubrics
6/6 packs
Use-case maps
0/6 packs
Pack context
6/6 packs
Available eval packs for Innovaccer
6 packs ready to run.
Agentic Guardrails Human In The Loop Gates Escalation
54 graded scenarios covering edge cases, failure modes, and quality checks.
Agents Of Care Autonomous Task Planning Tool Invocation
53 graded scenarios covering edge cases, failure modes, and quality checks.
Identity Resolution Unified Patient Record Empi
64 graded scenarios covering edge cases, failure modes, and quality checks.
Inbound Data Ingestion Interoperability
46 graded scenarios covering edge cases, failure modes, and quality checks.
Predictive Generative Ai Models
67 graded scenarios covering edge cases, failure modes, and quality checks.
Terminology Normalization Data Quality
63 graded scenarios covering edge cases, failure modes, and quality checks.
Why eval Innovaccer AI
Innovaccer'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 Innovaccer 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 Innovaccer's public product surface and runnable in Corsac with your own data.