Custom ML for problems an LLM cannot solve.
Forecasting, ranking, anomaly detection, and recommendations engineered on your data, with the rigour you would apply to any production system.
- Pilot to production
- 0-12wk
- Lift over heuristics
- 0-30%
- Pipeline reliability
- >0%
- Reproducible training
- 0%
Classical ML, applied properly.
Not every problem is a generative AI problem. Forecasting demand, scoring leads, detecting fraud, ranking results: these need real ML, with feature stores, eval pipelines, and reproducible training. We build them.
- Forecasting, ranking, classification, anomaly detection, recommenders
- Feature stores and reproducible training pipelines
- Production serving with monitoring and retraining triggers
From discovery to production.
- 01
Discover
Define the prediction, score the data, and pick the model family. Decide what beats the baseline.
- 02
Prototype with evals
Build the eval first. The model passes when it lifts the metric your business actually tracks.
- 03
Deploy
Production serving with feature stores, monitoring, and CI eval gates wired to your existing infra.
- 04
Operate
Drift detection, automated retraining, and an eval suite that compounds against new data.
Heuristics getting tired and not sure what ML can replace?
Book a 30-min consultWhat you get.
ML systems that survive contact with reality.
Drift detection on inputs and outputs, automated retraining with safe-deploy gates, and dashboards your data team can run. The system gets boring in the right way.
- Drift detection on inputs, predictions, and outcomes
- Automated retraining with safe-deploy gates and rollback
- Versioned features, models, and evals for full reproducibility
Common questions.
Get ML off the prototype shelf.
Free 30-minute consultation. We'll size the lift before you commit.
Schedule consultation