Smart Search & Discovery
Vector search with LLM re-ranking, tuned to your catalog — so "something for a rainy day gift under ₹2,000" actually returns the right products. (International: Vector search with LLM re-ranking, tuned to your catalog — so "something for a rainy day gift under $60" actually returns the right products.)
Default e-commerce search is keyword matching — type "blue shirt" and get every product with "blue" or "shirt" in the title, in no particular order of relevance. It breaks the moment a shopper searches in the way people actually search: "something casual for a summer wedding" returns nothing, even if you have exactly that product in stock.
How it works
- Vector search understands product meaning, not just keyword overlap — so a natural-language query maps to the right products even without exact word matches
- LLM re-ranking orders results by actual relevance to intent, not just text-match score
- Typo and synonym tolerance built in — "sneekers" finds sneakers without a manual synonym list to maintain
- Tuned to your catalog's actual attributes and categories, not a generic search index
Why this matters for conversion
A shopper who searches and gets a bad result rarely tries a second, more precise query — they leave. Every product a working search index fails to surface is a product that effectively doesn't exist for that visitor, no matter how good it is or how well it's priced. Smart search closes that gap: it finds what shoppers mean, not just what they literally typed.
Pairs with product recommendations
Smart Search and AI Product Recommendations draw on the same underlying product-understanding layer — search finds what a shopper is actively looking for, recommendations surface what they didn't know to look for. Building both together, rather than as separate bolted-on tools, is where the real lift in conversion comes from.
Frequently asked questions
- What's the difference between this and Shopify's built-in search?
- Shopify's default search is keyword-based and struggles with natural-language queries, typos, and synonyms. Smart Search uses vector search with LLM re-ranking, so it understands intent — a query like "gift for a coffee lover" returns relevant products even without an exact keyword match.
- Does this require a large product catalog?
- It works at any catalog size, though the relevance gain is most visible once you have enough products that keyword search alone starts producing noisy results — typically a few dozen SKUs upward.
- How long does it take to implement?
- Smart Search is scoped as part of a Growth or Pro AI e-commerce build; timeline depends on catalog size and is confirmed during discovery.
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