Fix silent revenue leaks: AI site search that finds what customers want

Many e-commerce SMBs suffer from "silent revenue leaks" when site search returns irrelevant results or no results at all, quietly depressing conversion rates and customer satisfaction; there are roughly 3,000,000 e-commerce SMBs globally, implying a $6.0B addressable market at an assumed $2,000 ACV. Merchants with catalogs of hundreds to tens of thousands of SKUs, limited search ops expertise, and high churn from poor search experiences are the primary customers. You could build an AI-driven site search platform that uses vector search and embeddings for semantic matching, automatic catalog ingestion and attribute mapping, relevance tuning, analytics, and out-of-the-box connectors for major platforms to enable a fast, low-friction install. Key engineering goals would be sub-100ms p95 query latency for good UX, transparent relevance controls for merchants, and an admin experience that turns search optimization into a few clicks rather than a consulting project. This market is attractive now because vector search and embeddings materially reduce zero-results and improve conversion, headless/composable commerce makes integrations feasible, and performance-first UX is a direct lever on revenue; independent scoring here is strong (Market Score 92/100, Revenue Potential 88/100) and competition is medium. To stand out you should focus on SMB-friendly productization: turnkey connectors, clear ROI metrics, simple tuning UX, and predictable pricing, while being honest about challenges such as embedding storage and compute costs, the need for high-quality catalog data, and the sales effort required to move conservative merchants—winning requires a balanced mix of product simplicity, technical performance, and pragmatic go-to-market partnerships.