PROJECT_005 // E-COMMERCE AI
RETAILMIND
// OVERVIEW
THE CHALLENGE
Traditional e-commerce sites show the same products to everyone: average conversion rates of 1-3%, 70% cart abandonment, and frustrated customers with irrelevant recommendations. Basic personalization based on history doesn't capture real-time intent.
Compaiser developed a predictive personalization engine that analyzes behavior in microseconds: clicks, time on page, mouse movements, context, and social trends to predict what products each visitor will buy.
// SOLUTION
HYPERPERSONALIZATION
We implemented neural collaborative filtering networks that process 147 behavioral signals in real time. The system learns from 2.4M daily sessions, continuously improving purchase predictions.
RetailMind increased conversion 3.2x, average order value +47%, and reduced cart abandonment to 34%. Currently processing 8.2M personalized recommendations daily.
// CONVERSION METRICS
3.2X
CONVERSION
8.2M
RECOM/DAY
340K
ACTIVE USERS
+47%
AOV INCREASE
// TECHNICAL STACK
RECOMMENDATIONS
- - Neural collaborative filtering
- - XGBoost for ranking
- - Real-time feature store
- - A/B testing framework
INFRASTRUCTURE
- - Next.js + React frontend
- - Redis for <1ms cache
- - Kafka for event streaming
- - MLOps pipeline (Kubeflow)
ANALYTICS
- - Real-time dashboards
- - Conversion funnel tracking
- - Automatic cohort analysis
- - ML revenue attribution
READY TO DEPLOY?
Compaiser is accepting applications for the next cohort of autonomous ventures.
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