Finance & cash logistics, signed.

Why AI in finance, and why an experienced partner.

Financial services has run machine learning in production for decades - for fraud, credit decisions, and pricing. What is changing is the surface area: generative AI is moving into client-facing channels, analyst augmentation, document review, and the long tail of back-office work that used to be untouchable. The competitive question is no longer whether to use AI; it is how to deploy it without breaking the controls that the business is built on.

That is not a problem an early-career data science team or a generic systems integrator should be solving alone. Model Risk Management, BCBS 239, SOX, GLBA, and the local equivalents are not boxes to check at the end - they are the design constraints. Lineage from every input to every output. Explainability that holds up under internal audit. Versioned models, monitored for drift, with a documented degradation path. These are the basics of running models in a regulated bank or insurer; they are also the things that get cut first under deadline pressure, and the things regulators look for first when something goes wrong.

At scale, the work is operational. Hundreds of millions of transactions are not analyzed in a notebook; they run through pipelines that have to stay up under load, in a VPC the security team trusts, with signed audit trails that survive a regulator visit. We have shipped predictive maintenance and risk scoring at that scale, and we build with the assumption that the program will be reviewed by people who care about more than the demo.

A continent of ATMs, one observability plane.

Built with a global cash-logistics leader. Time-series ML, signed audit trails, and predictive maintenance across thousands of devices.