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Rely on industry-proven AI quality metrics to increase reliability and trust

AI model developers from different business units may validate their models in different ways and may not report certain quality dimensions like fairness, explainability, accountability, or safety. Calvin’s comprehensive assessment provides developers and validation teams with consistent quality measures that enable transparency and decision making.

  • Increase validation efficiency to support rapidly increasing use cases, regulatory requirements, and customer expectations
  • Accelerate AI model deployment by meeting quality criteria
  • Identify models or business use cases with high risk exposure and analyze the root causes

Explore Quality Assessment and Automation features

Model Quality Metrics

Reduce validation time from several months to a few weeks by calculating a rich set of metrics with a single button click and investigating the main risk contributions to the aggregate score.

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Automated Model Onboarding

Provide an endpoint or upload model data and enter essential information once to start the assessments.

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Severity Assessment

Estimate economic losses based on the business use case. Select risk causes and effects based on ISO risk management standard 31000. Combine model risk frequencies and severity to obtain a unified portfolio risk distribution with time tracking and to discover the principal risk contributions across models, use cases, risk dimensions.

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How does Calvin Risk make AI model validation faster?

Calvin's QAA features streamline AI model validation by leveraging comprehensive, standardized quality metrics. With a single click, users can calculate a rich set of metrics, reducing validation time from months to weeks.

Can Calvin Risk help identify models or business use cases with high-risk exposure?

Calvin's Severity Assessment feature enables organizations to estimate economic losses and identify principal risk contributions across models and use cases. By leveraging ISO risk management standards and combining model risk frequencies with severity assessments, users gain insights into risk exposure and root causes, supporting informed decision-making.

What are the key dimensions covered by Calvin's AI quality metrics?

Calvin's AI quality metrics encompass essential dimensions including performance, robustness, fairness, explainability, accountability, and safety. Our methodology is grounded in industry data and developed in collaboration with ETH Zurich through extensive research and testing.

How does Calvin Risk estimate financial losses and value at risk for AI models?

Through our Severity Assessment, Calvin Risk enables organizations to estimate economic losses based on specific business use cases.This capability helps quantify potential risks associated with AI models,supporting risk management strategies and decision-making processes.

How does Calvin's Model Onboarding feature simplify the process of starting assessments?

With Model Onboarding, users can quickly provide endpoint details or upload model data, only needing to enter essential information points once to initiate assessments. This streamlined onboarding process ensures efficient integration with Calvin's quality assessment and automation capabilities.

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