Predictive maintenance & risk scoring

Not every problem benefits from a generative model - and in regulated environments, classical machine learning is often the right choice for both engineering and political reasons. Time-series forecasting, anomaly detection, risk scoring, fraud signals: these are well-understood disciplines with established methods, explainable outputs, and a body of regulatory precedent. They are also far easier to defend in a model-risk-management review than an LLM.

Where this pays off is the operational fabric of the business. Predictive maintenance across thousands of connected devices catches failures days before downtime, turning a reactive maintenance schedule into a planned one. Risk scoring, deployed inside your VPC, gives underwriters or compliance teams a calibrated signal they can defend to a regulator with full lineage from input to output. Fraud detection, tuned to your portfolio, reduces the false-positive load that exhausts your investigations team.

The discipline that matters here is operational, not algorithmic. Models drift. Distributions shift. Inputs go stale. We treat models as part of the operational fabric - versioned, monitored, on-call when they degrade, and tied to a signed audit trail so every score traces back to the inputs, the model version, and the moment in time it was produced. That's the difference between a model in production and a model in a notebook.