Overview
We build machine-learning models that derive business-relevant predictions and patterns from your data — from sales forecasting through customer segmentation to anomaly detection.
Our models run where your data lives: in Python-based ML pipelines, in SAP analytics scenarios, or as a feature inside your existing applications.
We cover the entire ML lifecycle: data exploration, feature engineering, model training, validation, and production deployment with monitoring.
Typical use cases
- Sales and demand forecasting for production and sales planning
- Customer segmentation and churn prediction
- Anomaly detection in transaction and operational data
- Recommendation engines for cross-/up-selling
- SAP-integrated analytics scenarios with ML models
Technologies
More focus areas
Data Engineering & ETL
Robust pipelines and integration with DataStage, SSIS, Oracle ODI, Azure Data Factory and Spark.
Learn more →Data Warehousing & Lakehouse
Central data models with SAP DWC, BigQuery, Microsoft Fabric and Hadoop/Cloudera.
Learn more →BI & Visualization
Dashboards and reporting with Power BI, Tableau, Superset, Oracle OBI, QlikSense and SAP SAC.
Learn more →