Realistic data, without the real-world constraints.

Make realistic data usable wherever real data is sensitive, unavailable, restricted or unsafe — and make every generated result verifiable.

AI, software and operational systems increasingly need realistic data, and the real data is frequently the one thing an organisation cannot safely or practically use. It carries personal information. It is contractually restricted. It is expensive to capture, or it describes a failure nobody wants to stage in production — a pump losing suction, an intruder moving through a control network, a rare clinical presentation.

Generating a plausible substitute is only half of the problem. The other half is being able to say, afterwards, exactly what was produced and how — which is why every RadMah run ends in an evidence record rather than a score.