High-stakes decision systems.

Where a model's output moves money, decides who gets credit, or has to answer to a regulator. The work that gets a system from a demo to something you can actually deploy.

The discipline.

A model that looks great in a notebook and a model you can trust with real consequences are not the same thing. What separates them is rigour, and rigour is the whole job.

We build decision systems that are accurate, calibrated, explainable, monitored, and robust. Robust against people actively trying to fool them, and against the way the real world keeps shifting under any model's feet. A model isn't finished when it ships. It starts drifting the moment the world it learned from moves on, and our work is keeping it honest after that, not just on launch day.

What we build.

01Real-time fraud and anomaly detection at scaleFinding the rare bad event hidden in a flood of normal activity, at millions of transactions and sub-second decisions. Tuned so fewer real cases slip through and fewer good customers get wrongly turned away. Those two move together. We don't catch more fraud by punishing your honest users.
02Forecasting under genuine uncertaintyPredictions that come with honest error bars, calibrated so a stated confidence actually means what it says. So you can size your bets against the truth, not against false comfort.
03Risk models built for scrutinyModels designed from the first line to hold up under regulatory review and to explain themselves in terms a reviewer can follow. Not a compliance layer bolted on the week before an audit.
04Decision support that sharpens judgementTools that lay out the evidence and the trade-offs, then leave the call with the person who's accountable for it. Built to avoid the quiet failure where a confident model talks a good analyst out of the right instinct.
05Model risk managementThe work that keeps a system trustworthy once it's live. Monitoring, drift detection, and governance, with a clear picture of how the model is behaving today, not how it behaved the day it was signed off. A model that has drifted in production is a different model from the one your documentation describes, even if nobody has touched the code.

What changes.

losses down, false positives down
Fraud losses fall, and false positives fall with them.
audit-ready
Models that explain themselves under review, with the evidence built up as you go rather than scrambled together at deadline.
Engaged by a global financial institution to rebuild fraud detection for calibration and explainability, inside a regulated environment where every decision the model made had to stand up to a reviewer, and keep standing up long after it went live.