Tools
Industry Solutions with Python
What Python can do across industries, based on analysis from open sources.
Our clients
Clients and partners
E-commerce and retail
Price and recommendations microservice alongside Pimcore
An open analysis of how to build a recommendation and dynamic pricing microservice in Python alongside Pimcore/PIM without changing the PIM core. Personalization and pricing logic live in separate services, while the PIM remains the source of truth for product content. We rely on open source: Gorse, LightFM, and NVIDIA Merlin for recommendations, and PyTorch/Gym for pricing policies, with REST/GraphQL and Pimcore Data Hub for data exchange. The business result: personalization and pricing experiments ship as independent releases, and PIM upgrades do not break integration.
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Manufacturing and distribution
Python for Demand Forecasting in Manufacturing and Distribution
An open analysis of how Python is used to build data pipelines and demand forecasts from historical data to plan procurement and inventory with predictive analytics. Using the open-source Nixtla statsforecast library and Databricks solutions for intermittent demand, with Croston, TSB, and ADIDA models and source links.
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Finance and Insurance
Python for scoring and anti-fraud in finance
An overview of an open Python stack for ML scoring and transaction anomaly detection: scorecardpy and optbinning for interpretable scorecards, PyOD and Isolation Forest for anomalies, Feast for real-time features, and the Jube platform for AML monitoring. We show how to build risk scoring and anti-fraud as a separate service without touching the core banking or insurance system.
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