Representative projects, each with the challenge, our approach and the outcome. Client details are kept confidential, and we're happy to share more under NDA.
A retailer needed weekly demand forecasts for hundreds of products. We built a pipeline that cleans sales history, adds calendar and promotion signals, and compares several models before choosing one.
How we checked it: Rolling-origin validation, MAPE and MAE
How we checked it: Precision, recall and alert lead time
How we checked it: Stratified cross-validation, ROC-AUC, precision / recall
How we checked it: Hold-out test set, MAE, RMSE, R²
How we checked it: Silhouette score and stability checks
How we checked it: Checked against analytical solutions
The methods are the same across sectors. We adapt the data work and the explanations to how your team operates.
Demand forecasting, customer segmentation and churn models.
Sensor analytics, fault detection and process simulation.
Risk scoring, anomaly detection and reporting dashboards.
Simulations, publication-quality figures and statistical analysis.
Operational data analysis, trend reporting and classification.
Data-driven features and clean websites, built quickly.
A model that looks good on paper but fails on new data is worse than no model. These habits keep results reliable.
Models are tested on data they have never seen, and time series use rolling-origin validation.
MAE, RMSE and MAPE for numbers; precision, recall and ROC-AUC for classes.
Every model must beat a simple baseline to earn a place in the solution.
Feature importance and error analysis show why a model decides what it does.
Every project ends with something you can run, read and explain.
Tell us what you're working on. We reply within one business day with next steps and a clear scope.