Services
Machine Learning
Models that learn from history to forecast, classify, rank and recommend.
Overview
Machine learning fits statistical models to historical data so that they can make predictions on new cases. Typical tasks include forecasting a quantity, classifying an event, ranking options and detecting anomalies.
A model is only valuable when its predictions improve a decision. We therefore start from the decision and its costs, choose evaluation metrics that reflect them, and validate on data that mimics future conditions rather than on convenient random splits.
Problems we help with
- Forecasts that are accurate on average but wrong when it matters
- Models that perform well in a notebook and degrade after deployment
- Black-box scores that business users do not trust or cannot act on
- Data leakage that inflates apparent accuracy
Capabilities
Forecasting and time series
Hierarchical, probabilistic and event-aware forecasting for demand, capacity and cash.
Classification and scoring
Propensity, risk, churn and quality models with calibrated probabilities.
Anomaly detection
Unsupervised and semi-supervised methods for fraud, faults and data issues.
Recommendation and ranking
Next-best-action and ranking models tuned to business constraints.
Explainability
Feature attribution, partial dependence and plain-language reason codes.
Rigorous validation
Time-aware backtesting, leakage checks and stress tests.
How we approach it
- 01
Define the target
Agree what is being predicted, when the prediction is needed and what it costs to be wrong.
- 02
Prepare features
Build reproducible feature pipelines with documented definitions.
- 03
Model and validate
Compare simple baselines with advanced methods using honest backtests.
- 04
Hand over
Package the model with documentation, monitoring and retraining guidance.
Where it is applied
- Demand and inventory forecasting
- Credit, claims and operational risk scoring
- Customer retention and lifetime-value modelling
- Predictive maintenance from equipment signals
Outcomes to aim for
These are goals we design towards, not guarantees.
- Predictions tied explicitly to decision costs
- Transparent comparison against simple baselines
- Documented, reproducible model pipelines
- Clear criteria for when to retrain or retire a model
Frequently asked questions
Bring us a decision you want to improve.
Tell us about the problem and the data you have. We will reply with a candid view of where we can help.