Prediction & risk models.

Real-time scoring runs inside the workflow that creates the risk. At intake. At application. At submission. At checkout. The model returns a probability and a risk category in milliseconds. Your team or your platform acts on that score before the user leaves the page.

This works for healthcare intake, loan and insurance underwriting, and fraud detection. It works for any decision where late action costs more than early action.

We build the model, the API, the thresholds, and the monitoring.

Forecasting models read your historical data. They project forward and give you a range, not a single number. Each range has uncertainty bounds. You choose the time horizon that matches how you plan.

This works for call volumes, sales, inventory, traffic, capacity, and staffing. Models can include seasonality, holidays, promotions, and weather.

Most demand planning still happens in Excel. It does not have to.

Some patterns are too small, too complex, or too fast for people to catch every time. Detection models learn from labelled examples. Then they find those patterns at scale.

This works for anomalies in operational logs, conditions in clinical or sensor data, fraud in transactions, and quality control in manufacturing.

The model does not replace human judgement. It finds what people would otherwise miss.

Churn models read customer behaviour, usage, engagement, and support history. They predict the probability that a customer will cancel or downgrade.

Lifecycle prediction uses the same method across the whole customer journey. When will a user upgrade? When will a trial convert? When will engagement drop? Your team gets an answer they can act on in time.

We calibrate. We do not just classify.
A model that says "high risk" or "low risk" is a classifier. A model that says "0.73" with a confidence interval is a tool you can build decisions on. We calibrate every model against the data it will see in production. We show you the calibration curves.

We build for the workflow, not the demo.
The model is the easy part. The hard part is delivering it inside the system that needs it. We ship models as production APIs. We integrate them into the platforms your team and your users already use.

We monitor for drift.
Models get worse over time. Populations shift. Markets change. User behaviour changes. We build monitoring into every model. We track input drift, output drift, and performance against the original validation set. You find out before your team does.

We build for regulated environments.
We work under ISO 27001 and SOC 2 Type II. We deploy with HIPAA in mind for US healthcare. We handle PHI for European clinical data. Every prediction gets an audit trail. Every model version is saved. Every training pipeline can be run again.

A prediction model reads your most sensitive data. Patient records. Financial history. Customer behaviour. It then makes decisions that affect real people. We build as if that matters, because it does.

HIPAA
For healthcare and telemedicine platforms aimed at the US market.

GDPR and PHI handling
For European clinical data and cross-border data flows.

ISO 27001
Our own certified management system for information security.

SOC 2 Type II
Independently audited security controls for enterprise buyers.

Every prediction gets an audit trail. Every model version is saved. Every training pipeline can be run again. You can always show what a model was trained on, and why it made the decision it made.

Many machine learning projects never reach production. They live in notebooks. Someone demos them to the leadership team. Then they stop, because nobody can integrate them.

We define production-ready as four things.

A real integration. The system that needs the model calls it, at the moment the decision happens. We test it with production data. We monitor it in production.

Calibration you can prove. Not "the model performs well." We validate the calibration curves against real outcomes. A 0.7 means 0.7, and the threshold strategy holds when someone questions it.

Monitoring from day one. We track input drift, output drift, and performance. You learn that the model is degrading before your team does.

Compliance in the architecture. Audit trails on every prediction. Versioned model artifacts. Training pipelines you can run again. Role-based access. We build this in from the start, not at the end.