Conversational AI Semantic Search for E-commerce using Elasticsearch + RAG

We’ve built a modular, hybrid search architecture that combines Elasticsearch’s speed with Retrieval-Augmented Generation’s semantic depth, delivering context-aware, accurate answers in real time. It uses transformer-based query reformulation, BM25 plus k-NN for dual retrieval, generative models for synthesis, and a ranking-feedback loop for continuous optimization. In tests, it beat standalone Elasticsearch and RAG on precision, recall, and ranking, all while keeping latency around 310 ms. This shows that blending lexical accuracy with semantic richness is the key to next-generation, real-time search—and we’re already looking at scaling it for multimodal queries and personalized results