Someone types into your shop's search box: "comfortable trainers for standing all day". Maybe they work in a café, on a ward or behind a shop counter, and they don't care about the brand. Your catalogue has exactly what they need, but the product page says "foam midsole and cushioned insole". Neither "comfortable" nor "standing" appears anywhere, so the search returns nothing, or a list of random trainers in an order that has nothing to do with what they asked for.

The product was there all along. It was the search that let them down.

What semantic search is

Semantic search looks for meaning rather than matching words. Instead of checking which products contain the terms the shopper typed, it tries to work out what they mean and find the products closest to that idea, even when they're described in different words.

Under the bonnet, the system turns both the search and each product description into a kind of numerical fingerprint of their meaning. Two texts about the same thing end up with similar fingerprints even if they don't share a single word, which is how it can tell that "comfortable for standing" and "cushioned insole" have a lot in common.

What people search for in an online shop

Baymard Institute, which has spent years studying how people search online shops, sorts searches into eight types. Some are very specific, like an exact model name or a category ("sandals"). Others describe what the product is for ("wedding gift", "gaming laptop"), what problem the shopper has ("stained rug") or what it needs to work with ("Sony RX100 camera case"). In Baymard's most recent benchmark, from 2026, 56% of the sites it studied didn't adequately support what their users were searching for.

Where each one wins

If someone types "trainers" or an exact model name, keyword search is fast, precise and predictable: it returns what contains those words and doesn't invent connections. For a product code or reference, it's still the best tool.

Semantic search earns its keep when shoppers describe what they need in their own words, which is exactly where keyword search struggles: synonyms your catalogue doesn't use, situations ("for standing all day"), problems or intentions. That's exactly how someone searches when they don't yet know which product they want.

That's why the usual approach is to combine them: keywords take care of exact matches, and meaning covers everything else.

A close match isn't always the right one

If someone searches for "iPhone 15 case" and you don't stock one, a system that relies on meaning alone may quite happily return the iPhone 14 case, because the two descriptions are very alike. It's no use to them, though: it won't fit their phone.

For compatibility, size or measurement searches, close isn't good enough. A good search engine has to respect those limits, and when nothing meets what the shopper asked for, it's better to say so than to show something similar as if it were what they wanted.

Searching better isn't the same as helping people choose

Even when it works well, semantic search still returns a list. A better-ordered list, with more relevant products, but the shopper still has to decide between them. For someone looking for trainers to get through an eight-hour shift, going from no results to twelve good options is a big step forward, even if they still don't know which of the twelve is right for them.

That second part, the choosing, is what an AI shopping assistant handles: it uses search to find the products that fit, then asks about whatever's missing and recommends one or two, explaining why.

What semantic search can't make up

Searching by meaning can only find meaning that's written down somewhere. If your product pages just say "black trainer, sizes 3–11", no system will guess they're comfortable for standing all day. Pages that explain what a product is for, who it suits and where it works well help your customers when they read them, and they help any search that's trying to understand them too.

SmartShop AI, a sales assistant for Shopify and WooCommerce stores, works as a chat alongside your shop's own search box: when a shopper writes to it, it searches your catalogue by keyword and by meaning at the same time, then recommends only products you sell and have in stock.