Models that go live and stay live.
We build ML models that solve real business problems: demand forecasting, churn prediction, anomaly detection, recommendation engines. Trained on your data, deployed to production, retrained automatically as new data arrives.
What we build
- Demand forecasting with scikit-learn, XGBoost, or Facebook Prophet — connected to your ERP
- Churn prediction models with SHAP explanations so the sales team understands the output
- Anomaly detection on financial transactions, sensor data, and operational logs
- Recommendation engines using collaborative filtering or embedding-based approaches
- MLflow for experiment tracking, model versioning, and reproducibility
- Model serving via FastAPI endpoints or SageMaker / Vertex AI / Azure ML managed services
- Automated retraining pipelines triggered by data drift detected with Evidently or WhyLabs
- Feature stores (Feast or Tecton) for consistent features between training and serving
How we work
Define the business problem precisely
We resist the urge to jump to algorithms. We first define the exact decision the model should improve, the success metric, and the cost of false positives vs. false negatives.
Audit and prepare the data
We assess data quality, coverage, and label availability. We tell you honestly if the data is not ready — and what it would take to get there.
Experiment and select the model
We run structured experiments tracked in MLflow. We test baseline models before complex ones. The model that ships is the one that performs best on your hold-out set, not the most impressive algorithm.
Deploy to production
We deploy models as REST APIs (FastAPI + Docker) or via managed ML platforms. We set up monitoring for prediction drift, data quality, and business metric impact.
Automate retraining and governance
We build automated retraining pipelines triggered by drift or schedule. We document model cards for governance and set up A/B testing infrastructure for comparing model versions in production.
Frequently asked questions
How do we know if we have enough data for ML?+
It depends on the problem. Demand forecasting typically needs 12–24 months of history. Churn prediction needs labelled examples of churned customers. We assess this in discovery before proposing any ML work.
How do you prevent models from degrading over time?+
We build monitoring pipelines that track data drift (with Evidently) and prediction quality metrics. When drift exceeds a threshold, automated retraining triggers. We also set up manual review checkpoints for high-stakes models.
Do the models need to run in real time?+
Most business ML use cases do not require sub-second inference. Batch predictions (daily or hourly) via Airflow jobs are simpler, cheaper, and more reliable. We design for the actual latency requirement, not the most impressive architecture.