A shopper lands on your store and types "flowy midi dress for a wedding guest." Your search bar returns zero results. Or worse, it shows a random assortment of products that have nothing to do with what she asked for. She leaves. You paid to get her there.
This is what keyword-based search does. It matches words, not intent. And when your catalog has hundreds or thousands of products, the gap between what shoppers type and what your search returns is where revenue disappears.
If you've started looking at AI search apps, you already know the default Shopify search has limits. It runs on keyword matching, handles a handful of filter types, and gives you almost no data on what customers are actually searching for. The harder question is how to evaluate the AI search apps competing for your attention. There are over a dozen on the Shopify App Store, and their marketing pages all say roughly the same things.
Here are the five criteria that actually separate the good ones from the expensive disappointments.
1. Does it understand what your customers mean?
Most merchants skip this step entirely, and it's the one that matters most.
Keyword search matches the exact words a customer types against your product titles and descriptions. AI-powered semantic search is supposed to understand the intent behind the query. The difference matters when a shopper types "comfortable black shoes for standing all day" and your products are tagged as "cushioned support flats" or "ergonomic work shoes." Keyword search returns nothing. Semantic search should connect those dots.
When you're testing a search app, run these queries before you commit:
Start with a natural language phrase, not a product name. Something like "lightweight jacket for spring travel" or "gold earrings under $50." Does the app return relevant results, or does it choke on anything beyond two-word queries?
Then misspell a product name deliberately. Type "cashmeer sweater" or "earing" and see what happens. Typo tolerance sounds basic, but plenty of apps still fail this test.
Finally, search for an attribute that isn't in the product title. If you sell dresses and a customer types "v-neck," does the app know which products have v-necklines even if "v-neck" isn't in the title? This tells you whether the search understands your catalog beyond scanning text fields.
At PersonalizerAI, we train custom AI models on each merchant's specific catalog, order history, and customer behavior. The search learns the relationships between products, attributes, and how your customers actually shop. When someone searches "complete outfit for date night," the results factor in what your customers have actually bought together, not a keyword match against product titles.