Modern e-commerce search engines often fail to understand user intent, leading to irrelevant results and poor user experience. In this project, we develop an AI-powered search enhancement system designed to improve product discovery by interpreting natural language queries. The system leverages advanced language models to analyze user input, identify intent, and match it with the most relevant products in real time. Instead of relying solely on keyword matching, our solution understands context, preferences, and implicit meaning. The product is designed as a scalable B2B solution that can be integrated into existing e-commerce platforms to increase conversion rates and user satisfaction. By bridging the gap between user intent and search results, our system provides a smarter and more intuitive shopping experience.