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Predictive analysis with machine learning

We transform your historical data into operational forecasts. We build supervised and unsupervised machine learning models to anticipate demand, reduce churn, identify anomalies, and optimize business decisions with a measurable confidence level.

Machine learning models to anticipate market trends, user behavior, and reduce operational risks.

Use cases

  • Sales forecasting and retail stock management
  • Churn prediction for telco and SaaS
  • Industrial predictive maintenance
  • Anomaly detection on financial transactions
  • Credit scoring and anti-fraud

Measurable benefits

  • More stable forecasts than manual estimates
  • Identification of churners before they cancel
  • Measurable data-driven decisions
  • Traceable ROI for every model in production

Technical details

Models and algorithms

  • Time series: ARIMA, Prophet, NeuralProphet
  • Gradient boosting: XGBoost, LightGBM, CatBoost
  • Deep learning: LSTM, Transformer
  • Clustering and PCA for segmentation

Data pipelines

  • Automated feature engineering
  • Dataset versioning with DVC
  • Validation with temporal cross-validation
  • A/B testing of models in production

Deploy and monitoring

  • Real-time or batch model serving
  • Automatic drift detection
  • Scheduled retraining
  • Explainability with SHAP/LIME

Visualization

  • Custom dashboards with React + D3
  • Power BI, Tableau, Looker integration
  • Email/Slack alerts on thresholds
  • Executive reporting PDF export

How we build a forecasting model that is actually used

  1. Define the decision — Before the model we define which decision must improve: how much to order, when to intervene, which customer to contact. A forecast that changes no action is useless.
  2. Historical data check — We assess depth, continuity and reliability of history, including breaks caused by system or catalogue changes.
  3. Baseline — We first build a simple reference (moving average, seasonality, existing rule): a model is worth it only if it beats the baseline consistently.
  4. Features and training — We add variables the business already uses to decide — calendar, promotions, weather, lead time — and train on separate time windows.
  5. Temporal validation — We test on periods the model has never seen, not random samples, and measure error in the metric that drives the decision.
  6. Operationalisation — The forecast enters the workflow with thresholds, a visible confidence interval and a logged human override.
  7. Drift monitoring — We compare forecast and actuals over time; when error exceeds the agreed threshold the model is retrained or revised.

We deliver training datasets, reproducible pipelines, validation metrics and the retraining procedure.

Working with us

The team that analyses the process is the team that builds and maintains it: product, engineering, integration with your existing systems, governance of automated decisions and post-release support.

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FAQ

How much data is needed for a predictive model?

It depends on the problem. For sales forecasting, at least 18-24 months of history are needed. For classification, even a few thousand labeled examples.

Are the models explainable?

Yes. We use explainable AI techniques (SHAP, LIME) to show which variables influence the prediction — essential in regulated environments.

How do you handle data quality?

We build data quality pipelines with automated validation, anomaly alerting, and dataset versioning.