AI-powered product search that understands intent
We replaced keyword search with a semantic search engine that understands natural language queries like "lightweight summer dress for a beach wedding", dramatically improving conversions.
Where they started.
ShopWave, a fashion e-commerce brand with 80,000 SKUs, relied on traditional keyword search. Shoppers typed "blue dress" and got hundreds of irrelevant results. Conversion from search was 40% lower than from category browse. They needed search that understood intent, "date night dress under $100" or "comfortable work blazer", not just keywords.
How we solved it.
We analyzed 6 months of search query logs to understand how shoppers actually describe what they want. We found that 60% of queries were natural language, not keyword lists.
We built a product embedding model trained on titles, descriptions, attributes, and images. We used a multi-modal approach so visual similarity (color, style, occasion) was captured.
We implemented semantic search: queries are embedded and matched against product embeddings. We added filters for price, size, and category that apply post-retrieval.
We A/B tested against their existing search. We measured click-through rate, add-to-cart rate, and conversion, not just relevance scores.
We deployed with a hybrid fallback: semantic search for queries over 3 words, keyword search for short queries, with smooth handoff.
The outcome.
40% improvement in search relevance (measured by click-through and conversion from search results).
25% increase in average order value from search, shoppers found higher-intent products faster.
Search-to-purchase conversion increased from 2.1% to 3.4% within 8 weeks of launch.
The semantic layer is now used for "You might also like" and category recommendations.
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