Case Study: How We Built a Hyper-Personalized E-commerce Recommendation Engine

A leading e-commerce client struggled with generic 'customers also bought' sections. We developed an LLM-based recommendation engine that analyzed product descriptions, user reviews, and individual browsing history to understand context and intent. The system could infer style preferences, budget constraints, and even suggest complementary products for a specific occasion mentioned in a review. The result was a 22% increase in user engagement with recommendations and an 18% uplift in average order value. This is personalization that drives real revenue.