AI Personalization Engine Lifts Add-to-Cart Rate 34% for Mid-Market Fashion Retailer
SpeedMVPs built a real-time AI personalization engine on their existing Next.js storefront backed by pgvector on Supabase for product and user embeddings. A collaborative-filtering model trained on 18 months of order and clickstream data was deployed via a FastAPI microservice, surfacing ranked recommendations per user in under 80ms. Personalized search reranking was layered on top of their existing Algolia index using a lightweight LLM-based intent classifier that rerouted ambiguous queries to semantically similar in-stock products.
Project Overview
What We Built
- SpeedMVPs built a real-time AI personalization engine on their existing Next.js storefront backed by pgvector on Supabase for product and user embeddings. A collaborative-filtering model trained on 18 months of order and clickstream data was deployed via a FastAPI microservice, surfacing ranked recommendations per user in under 80ms. Personalized search reranking was layered on top of their existing Algolia index using a lightweight LLM-based intent classifier that rerouted ambiguous queries to semantically similar in-stock products.
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