The workhorse of production machine learning — gradient boosting pipelines built on your data, engineered for real-world reliability.
Most production ML problems are tabular — structured rows of features predicting an outcome. Customer churn, equipment failure, demand forecasting, credit risk, price sensitivity.
Gradient boosting (XGBoost, LightGBM, CatBoost) dominates this space for good reason: it handles mixed feature types, tolerates missing values, captures nonlinear interactions without hand-engineering, and consistently outperforms more complex alternatives on structured data of modest size.
But the algorithm is rarely the limiting factor. The real work is in feature engineering that doesn't leak information across time boundaries, probability calibration for decision-making contexts, and building an evaluation framework that measures what actually matters for the business — not just held-out accuracy on a static test set.
We train every model on your operational data, with your feature set, against your specific prediction target. The result is a model that reflects the patterns in your domain, not a generic benchmark.
How we approach it
Defining the prediction target, time boundary, label construction, and success metric before touching data. Most ML projects fail not because of the model — but because the prediction task was framed in a way that doesn't map cleanly to the business decision it's meant to support.
Understanding distributions, missing-data patterns, cardinality, and temporal structure. Building features that capture genuine signal without introducing leakage — especially in time-dependent settings where training and inference data must be treated differently.
Gradient boosting as the default starting point. Logistic regression or shallow trees where interpretability, regulatory requirements, or data volume call for it. Proper cross-validation schemes that respect temporal structure and avoid optimistic hold-out splits.
Ensuring predicted probabilities are meaningful for downstream decisions — not just well-ranked. Platt scaling or isotonic regression where the raw model is overconfident. Threshold optimisation against your actual cost function, not a default 0.5 cutoff.
SHAP-based feature importance for stakeholder communication and sanity-checking model behaviour. Full feature pipeline specification so production engineering can reproduce inference exactly. Monitoring schema and retraining criteria defined before deployment.
What the model learns
Feature importance
SHAP feature importance — which signals the model relies on most. Trained on your data, these rankings reflect your domain, not a generic benchmark.
Tell us what you're trying to predict and we'll scope it together — no obligation.
Start a conversation →