Mirra is a healthcare AI evaluation platform that gives developers access to over 75 million real patient records from the Global South, enabling them to test and improve models against populations they otherwise couldn't reach. The platform provides benchmark reports, mapped failure modes, and synthetic training datasets to close performance gaps before deployment.
Funding
Funding not disclosed
Founders
Product
Problem
Over 70% of healthcare AI training data originates from just three countries—the United States, China, and Germany—yet models built on that data are deployed globally. This leaves healthcare AI systems significantly less accurate for patients in other regions, and developers often have no legal way to access representative data from the Global South due to data sovereignty laws and institutional governance restrictions.
Solution
Mirra provides a protected evaluation environment where trusted institutions contribute governed datasets and developers bring their models to be tested against real-world and synthetic populations they couldn't otherwise reach. The platform simulates deployment scenarios to identify failure modes before models reach actual patients, then generates benchmark reports and synthetic training datasets designed to close the exact gaps discovered. Every evaluation produces actionable insights that developers can take back to their own infrastructure for further training and improvement. This approach ensures healthcare AI models are validated for diverse populations before deployment, making "state of the art" mean state of the art for everyone.
Target Audience
Primary customers are frontier AI labs, healthcare AI companies, academic and research institutions, pharmaceutical companies and CROs, and health systems that need to validate their models against diverse, representative patient populations before deployment.
Features
- Access to 75M+ real patient records from across the Global South, with more added quarterly
- Protected environment that respects data sovereignty laws while enabling model evaluation
- Adversarial testing infrastructure that breaks models before real-world deployment
- Benchmark reports that map specific failure modes across different population segments
- Synthetic training datasets generated from evaluation findings for developers to use on their own infrastructure
- Clinically grounded evaluation framework ensuring medical relevance and real-world complexity