Test AI systems before they become business risk
Calvin Risk tests AI systems across real-world scenarios, edge cases, adversarial inputs, and production workflows, turning behavior into measurable evidence for deployment, risk, and governance decisions.
AI systems pass demos, then fail in production.
Controlled tests rarely reflect production. Calvin Risk surfaces the failures that only appear at scale, before they reach customers, regulators, or the balance sheet.
The dimensions that determine trust.
metrics applied to systems and use cases, so that teams can compare, document, and act on AI behavior with confidence.
Performance & reliability
Accuracy, consistency, regression, and stability across scenarios, versions, and workflows.
Robustness & stress testing
Sensitivity to input variation, rare cases, unexpected interactions, and changing real-world conditions.
Bias & fairness
Systematic performance differences across user groups, case types, geographies, or decision contexts.
Security & safety
Jailbreaks, prompt injection, harmful outputs, hallucinations, and unsafe system behavior.
Explainability & grounding
Traceable reasoning, attribution, and source fidelity, including RAG faithfulness and groundedness.
Built for the workflows that carry real risk
Claims automation
Reliable extraction your operations can depend on at scale.
Document processing
Turn unstructured documents into a reliable source of business-critical data.
GenAI Copilots
Autonomy you can trust to act within defined bounds.
Voice & conversational AI
Keep AI interactions controlled and accountable.
From scenarios to governance evidence.
Scenarios
Real-world, edge & adversarial inputs
Evaluation
Run at scale across systems & versions
Risk signals
Performance, safety & fairness scored
Evidence
Reproducible, audit-ready records
Governance
Decisions, oversight & compliance
More than automated testing: an assurance layer operated with domain expertise.
Calvin Risk combines automated testing infrastructure with expert evaluation design, helping teams define what good AI behavior looks like and measure it continuously.
Run evaluations at scale
Infrastructure that executes evaluations continuously across systems and versions.
Define what should be measured
Domain experts set the criteria that make evaluation meaningful for your context.
Testing is the foundation for AI governance.
Every evaluation generates evidence that feeds into risk assessment, documentation, compliance workflows, and deployment decisions.
Audit-ready evidence
Turn test results into traceable records for reviews, audits, and regulatory documentation.
Continuous risk visibility
Track how risks evolve as systems, models, prompts, data, and workflows change.
Shared decision basis
Give engineering, risk, compliance, and leadership teams the same source of evidence.
"Dataset expansion with perturbations was fully handled for us – compliance tests were taken completely off our plate."
See how AI testing becomes operational evidence.
Learn about why AI risk appears differently across industries, workflows, and decision systems.
Ready to assure your AI systems? Learn how your systems behave and make them trustworthy by design.
Data & analytics
Ship systems that behave reliably
Risk & compliance
Establish continuous, defensible oversight
Business leaders
Scale AI without hidden risk