Machine learning models built for a decision, not a research paper
A model that's 94% accurate in a notebook isn't the same as a model that improves a real business decision under real data drift. We scope machine learning to a specific decision — churn prediction, demand forecasting, fraud detection — and evaluate it against the business metric that actually matters.
What’s included
Ideal for
- 01Problem framing & data audit
- 02Feature engineering
- 03Model development & training
- 04Evaluation against business KPIs
- 05Deployment & monitoring pipeline
- 06Retraining strategy
Frequently asked
Do you build custom models or use existing ML APIs?→
Depends on the problem — most use cases don't need custom training and are better served by proven approaches on your own data. We'll recommend custom training only when it's actually justified.
How do you handle model drift after deployment?→
Every ML engagement includes a monitoring and retraining strategy — a model evaluated once at launch and never revisited is the most common way these projects fail quietly.