Turn your data into competitive foresight
Machine Learning & Predictive AI
From custom model training to production MLOps pipelines, we build predictive systems that learn from your unique data and deliver measurable business outcomes.
Technology Stack
Case Study
E-Commerce
The Challenge
A mid-market retailer with 12,000+ SKUs was carrying $2.4M in annual overstock while simultaneously experiencing stockouts on 8% of high-velocity items. Their existing demand forecasting used a 12-week moving average — ignoring seasonality, promotions, and external signals.
Our Solution
We built a custom ensemble forecasting system combining gradient boosting (XGBoost) with a temporal fusion transformer trained on 5 years of transaction history, enriched with external signals (weather, holidays, social trend indices). The MLOps pipeline retrains weekly with new data using MLflow for experiment tracking and Kubernetes for scalable inference.
Results
- 97.3% forecast accuracy across all SKU tiers (up from 71%)
- $2.4M annual overstock cost eliminated in year one
- Stockout rate reduced from 8% to under 0.9%
- Model inference serves 12,000 SKU predictions in under 400ms
Technologies Used
"The model doesn't just forecast — it understands our business. It even caught a supplier lead-time issue before our buyers did."
— Head of Merchandising, E-Commerce Client
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