Machine Learning

Where a rule-based approach runs out of road, a trained model can pick up patterns in your own history. We build the model and, just as importantly, the data preparation and evaluation around it.

What we build

• Forecasting models for demand, capacity, churn and revenue
• Classification and scoring: risk, priority, eligibility, quality
• Segmentation and propensity models for marketing and service teams
• Feature pipelines and training data sets that can be rebuilt on demand

How we deliver it

• Start from a baseline: the simplest model that beats current practice
• Honest evaluation against held-out data, with the business metric stated up front
• Explainability so reviewers can see why a prediction came out the way it did
• Retraining schedules and drift checks once the model is live

Where it fits

• Decisions made today on gut feel or static rules
• Plenty of historical data that nobody has modelled
• Manual scoring or triage work that follows patterns