What is semantic search?
Semantic search matches what a shopper means, not just the words they type. A search for "summer dress" finds sundresses, linen midis, and beach cover-ups even when no product title contains the word "summer". Keyword search needs the exact letters; semantic search understands that two phrasings point at the same product.
How does semantic search work?
Say your store carries 40 dresses. The titles read "Linen Midi", "Floral Wrap", "Tiered Maxi". Not one says "summer". A shopper types "summer dress" and keyword search returns nothing: those letters appear nowhere in your catalog. Semantic search maps words to meaning instead. It knows "summer dress" sits close to lightweight fabrics, short sleeves, and warm-weather styles, so the linen midi at $79 shows up first. Same catalog, same shopper, one sale exact-match searching would have dropped.
On Shopify stores
Shoppers describe products in their own words, and those words rarely match your titles. You write "Merino Quarter-Zip"; the shopper types "warm top for hiking". The gap runs wide: titles get written for catalogs and SEO, not for the phrases real people type into a search box. Semantic search closes that gap without renaming products or hand-maintaining a giant synonym list.
Related terms
Semantic search with Sledge
Semantic search is built into Sledge's search: queries match by meaning, not just keywords, so "summer dress" finds the sundress and "cozy gift" finds the throw blanket. Conversational queries go further, turning "running shoes under $50" into a category plus a price filter. And synonyms, written by you or AI-suggested for your approval, catch the matches meaning alone might miss.
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