Calvin Risk develops a risk management platform that quantifies and mitigates the risks associated with artificial intelligence algorithms, ensuring compliance with the EU AI Act. The platform centralizes AI model governance, providing transparency and facilitating the assessment of potential negative impacts across industries such as insurance, banking, and telecommunications.
Funding
$5.6M raised to dateRaised to date based on public sources. This may differ from the amount the company actually raised and is based only on what is publicly available on the internet.
JCSSFounders
Product
Problem
Enterprises face challenges in governing AI models, ensuring compliance with regulations like the EU AI Act, and quantifying potential risks associated with AI deployments. Lack of transparency and centralized oversight can lead to compliance violations, ethical concerns, and financial losses.
Solution
Calvin Risk offers an AI governance platform that enables organizations to manage AI risks, ensure regulatory compliance, and maintain transparency across their AI systems. The platform provides a centralized inventory of AI systems, captures essential governance information, and automates compliance workflows. By quantifying AI quality and potential risks, Calvin Risk empowers enterprises to make informed decisions, mitigate negative impacts, and achieve responsible AI adoption.
Target Audience
The primary target audience includes enterprises across financial services, transportation, telecommunications, and other industries that are deploying AI and need to comply with AI regulations like the EU AI Act.
Features
- AI Inventorization: Creation of a centralized inventory of AI systems with essential governance information, including EU risk level and system ownership.
- AI Transparency: Transparent AI model governance, providing visibility into AI systems' design, functionality, and decision-making processes.
- AI Compliance: Streamlined compliance with the EU AI Act through automated workflows and adherence to regulatory standards.
- AI Quality Assessment: Quantification of AI quality across dimensions like performance, robustness, fairness, and explainability.
- Risk Quantification: Monetary quantification of model uncertainty in machine learning and AI.
- GenAI Metrics: Assessment of GenAI system quality to address performance, robustness, fairness, and explainability issues.