Keeping AI under control is getting harder.

Modern ML models are complex by nature: detecting biases, measuring risks, and tracing unwanted behaviors requires methods that go beyond standard statistical analysis.

Training data is often large, noisy, and assembled through opaque pipelines. When a model produces an unfair outcome, identifying the root cause is rarely straightforward.

Regulatory pressure is growing. The EU AI Act introduces strict requirements for high-risk AI systems, demanding continuous monitoring across multiple risk dimensions.

MIRAI combines formal methods grounded in published academic research with applied data science. This means our analyses are not black boxes: every metric we produce is explainable, traceable, and defensible.

We work at the intersection of logic, mathematics, philosophy, and computer science. Our team includes researchers, engineers, and domain experts who collaborate at every stage of an engagement.

We work with private organizations and public administrations that deploy or procure AI systems in high-stakes domains: credit scoring, HR, fraud detection, healthcare, and public services.