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Is Calvin used for development or post-deployment?
Both. Calvin supports AI systems throughout their entire lifecycle. In development, it enables organizations to document, assess, validate, and govern AI systems before they go live. Post-deployment, it provides continuous monitoring on model performance and drift to ensure systems remain enterprise-grade, compliant, and fit for purpose.
What is the difference between a model inventory versus a use case inventory?
A model inventory is a detailed list of all AI models the organization is developing or has deployed, focusing on the technical aspects such as training data, performance metrics, and version history. In contrast, a business use case inventory is a catalogue of the business applications, compliance material, and governance processes related to these AI models.
How is Calvin Risk different from manual AI testing?
Manual testing relies on ad hoc scripts and spreadsheets that limit teams in scaling and achieving enterprise-grade monitoring. Calvin automates model onboarding, runs standardized quality and risk metrics on every model, and keeps a continuously updated inventory and evidence trail, leading to efficiency gains of over 80%.
What are the key dimensions covered by Calvin's AI quality metrics?
Calvin's AI quality metrics encompass essential dimensions including performance, robustness, fairness, and explainability. Our methodology is grounded in industry data and developed in collaboration with ETH Zurich through extensive research and testing.
How does Calvin Risk make AI model validation faster?
Calvin's quality assurance features streamline AI model validation by leveraging comprehensive, standardized quality metrics. With a single click, users calculate a rich set of assessment metrics, reducing validation time from months to weeks.
What is "model drift" and how does Calvin detect it?
Model drift is the degradation of an AI model's performance or behavior over time as real-world data shifts away from what it was trained on. Calvin's post-deployment monitoring re-runs assessments on live models to catch drift before it causes business or compliance incidents.
How does Calvin Risk support governance teams?
Our platform enables you to capture and assess AI governance requirements, evidence files, risk levels, ownership details, and other data for each use case, ensuring transparency and accountability throughout its lifecycle. Beyond building trust, Calvin establishes a consistent governance playbook across the organization by digitizing manual processes and centralizing AI inventory, documentation, and workflows under a single automated platform.
Can Calvin Risk assist me in preparing for internal and external audits?
Our platform facilitates audit readiness by capturing comprehensive governance information related to AI models and use cases. You can easily access assessment logs, configuration details, and customization options, providing auditors with the necessary insights and documentation for compliance checks.
How can Calvin Risk help me comply with the EU AI Act?
Our Governance suite enables organizations to identify and meet obligations under frameworks such as the EU AI Act by organizing development, deployment, and governance data in accordance with regulatory standards as well as providing tailored assessments to generate requested evidence.
Does Calvin Risk integrate with our existing tech stack (i.e., model registries, monitoring tools)?
Yes. Calvin integrates with enterprise systems through custom integrations, with additional API-based connectivity available where required.