RetailMind — AI Recommendation Engine
35% increase in AOV
Client
RetailMind
Industry
E-commerce
Timeline
2 months
Team Size
3 engineers
What We Were Up Against
RetailMind's generic "you may also like" recommendations were driving less than 2% click-through rates. They needed a sophisticated personalization engine that could dramatically increase AOV.
How We Solved It
We built a real-time recommendation engine using collaborative filtering and LLM-enhanced product understanding. Redis caches personalized recommendation vectors, with a FastAPI service delivering sub-50ms recommendations at scale.
What We Built
Real-Time Personalization
Sub-50ms personalized recommendations at scale
Behavioral Analysis
Deep analysis of browsing and purchase patterns
Cross-Sell Engine
Intelligent bundling and upsell opportunity detection
A/B Testing
Built-in experimentation framework for optimization
The Numbers.
Increase in AOV
Recommendation CTR
Additional Monthly Revenue
Built With
“
RetailMind's recommendation engine paid for itself in the first week. Our AOV jumped 35% immediately after launch.
Aisha Patel
Head of E-commerce, RetailMind
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