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Predictive ML & Forecasting
Much of the value in machine learning still comes from predicting a number or a category from historical data: demand next month, which lead will convert, whether a transaction looks wrong. We build predictive systems that beat simple baselines by a margin you can measure, and we tell you honestly when they don't.
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What we deliver
- Baselines first, so every model has to earn its complexity
- Feature engineering and pipelines that are the same in training and production
- Forecasting for demand, capacity and revenue
- Ranking and recommendation systems
- Anomaly and fraud detection
- Explainability, so decision-makers can see why a prediction was made
Where it's used
Demand and inventory forecasting
Lead scoring and churn prediction
Pricing and risk models
Predictive maintenance
How we work
- Discover. We look at your data, define success metrics and agree what "good" means before building anything.
- Build. We start with a baseline, iterate with measured evaluation, and keep you in the loop with working demos.
- Ship & monitor. We deploy with tests and rollback plans, then monitor quality, cost and drift.
Tools we use
scikit-learnXGBoostLightGBMProphetPyTorchSparkMLflow
Questions
How accurate will the model be?
We can't promise a number before seeing your data. What we do is establish a baseline and an evaluation method up front, so improvements are measured against something real.
What if we have little history?
We'll say so. Sometimes the right first step is collecting the right data, or using a simpler rules-based approach while history builds.