Customer Stories
Dema.ai Drives Profitability and Growth with a Smarter Forecasting Platform
How dema.ai partnered with Xebia to build and productionize a scalable sales forecasting model in record time.
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At a Glance
Challenge
Build a reliable and scalable sales forecasting solution to optimize inventory, resources, and profitability.
Solution
Designed and implemented time-series ML models using Nixtla and Kedro, deployed in Kubernetes with full monitoring and automation.
Results
Productionized a new forecasting capability in less than 2 months to deliver cost-efficient, automated daily forecasts, outperforming benchmark models.
The Client
Dema.ai is a fast-growing e-commerce platform that provides businesses with real-time intelligence on marketing performance, customer behavior, inventory, and sales. Its mission is to help clients turn data into decisions that boost revenue, increase retention, and achieve sustainable profitability.
The Challenge: Enabling Clients to Overcome Forecasting Complexity to Stay Competitive
Accurate sales forecasting drives e-commerce business success as uncertainty, complex interdependencies, and strong seasonality affect demand patterns. Poor forecasts can be costly: overstocking leads to higher storage costs and wasted inventory, while understocking results in lost sales and frustrated customers. Dema.ai wanted to add a robust forecasting capability to its platform to give clients the ability to plan more effectively and sharpen their competitive edge. Machine learning models need to be integrated into production environments, monitored continuously, and updated to prevent data drift.
The company needed a forecasting engine that was accurate, scalable, automated and easy to maintain — all without vendor lock-in.
Solution: Lean, Automated Forecasting Platform Built for Speed and Accuracy
Xebia partnered with dema.ai to turn a proof of concept into a production-ready machine learning solution in under two months. The team built time-series forecasting models using the Nixtla’s open-source suite to experiment efficiently and achieve high predictive performance. Kedro provided reproducible, engineering-grade pipelines that ensured scalability and adherence to software best practices.
Designed for scalability and efficiency, the solution was deployed on Kubernetes. Forecasting jobs were orchestrated as Kubernetes cron tasks using a scale-to-zero approach to optimize efficiency and minimize infrastructure costs. It enabled real-time monitoring with Prometheus and Grafana, while MLflow tracked daily model performance. The result was a lean, automated forecasting platform with end-to-end visibility into pipeline health and performance.
Result: Accurate, Automated, and Scalable Forecasts
In less than two months, Dema.ai gained a production-ready forecasting engine that outperformed benchmarks, delivering automated daily predictions at minimal cost.
- A production-ready forecasting capability delivered in less than 2 months.
- Forecasts that consistently outperformed benchmarks.
- Automated daily predictions at minimal cost through scale-to-zero Kubernetes jobs.
- Full monitoring and transparency with Prometheus, Grafana, and MLflow.
Looking Ahead
With robust forecasting engine now embedded in its platform, Dema.ai is exploring advanced use cases such as demand sensing, promotional impact analysis, and multi-vertical forecasting. The scalable architecture ensures the solution will continue to evolve, keeping Dema.ai ahead in the e-commerce intelligence market.
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