European E-commerce Platform
Built a real-time personalization engine serving 2M+ daily active users with AI-powered recommendations
The Challenge
A fast-growing European e-commerce platform with 2M+ daily active users was struggling with generic product recommendations. Their rule-based system had low click-through rates and couldn’t adapt to real-time browsing behavior, resulting in missed revenue opportunities.
Our Solution
Kameleon Labs rebuilt the recommendation engine using a multi-model approach: real-time behavioral analysis agents, collaborative filtering, and contextual ranking. The system processes browsing events in real-time and serves personalized recommendations in under 50ms.
Our Approach
Discovery
2-week analysis of existing recommendation performance and user behavior data
Prototype
3-week A/B test comparing AI recommendations vs existing rule-based system
Production
5-week build with real-time data pipeline and CDN-optimized serving layer
Optimization
Ongoing model retraining, A/B testing, and feature expansion
Results
Tech Stack
“The personalization engine has been transformative. Our customers are seeing products they actually want, and the revenue impact speaks for itself.”