Machine learning that runs reliably in production.
Forecasting, prediction, and anomaly detection, with the engineering to keep models accurate after launch.
Models work in a notebook and fail in production. Data drifts, retraining is manual, monitoring is missing, and nobody can explain a decision to a regulator or customer. The cause is usually the engineering and data around the model.
We build models on governed data, set up MLOps for training, deployment, monitoring, and retraining, and document how each model works and where it falls short. We use the simplest model that solves the problem.
Models that stay accurate and that you can explain.
Starting points we adapt to your data and systems. You keep what we adapt.
Pipelines to deploy, monitor, and retrain machine learning models.
Check whether your data can support a decision before you fund the work.
We take one model, new or existing, and put it on a release path: training from code, tests against agreed thresholds, approval by someone other than the builder, monitoring for drift and accuracy, and a tested rollback. Then the next model uses the same path.
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