AI-Powered Search & Recommendation Engine for E-commerce Marketplace

Challenge

The marketplace's keyword-based search engine was failing users. Queries like 'comfortable work-from-home chair' returned irrelevant results because the system couldn't understand intent. Product discovery was broken, with 40% of searches resulting in zero relevant clicks. The recommendation engine was rule-based and couldn't adapt to individual user behavior.

Solution

We implemented a hybrid search architecture using MongoDB Atlas Search for full-text queries combined with Redis-backed vector search for semantic understanding. Product embeddings capture meaning beyond keywords, so 'comfortable WFH chair' matches ergonomic office chairs. A real-time recommendation engine runs on user behavior signals cached in Redis, with the entire system deployed on AWS EKS and monitored through Datadog.

Results