Product
Your discovery agent, on your store.
AI Search is natural-language search with guided Discovery Prompts and filters that need no setup, because the catalog underneath is clean. You can pin, boost, or bury products and rank by margin or stock, and you never have to write rules or tag products by hand.
Not ready? Score your store free first →
Merchandising & ranking
You control what ranks, without touching the catalog.
Because the catalog underneath is structured, merchandising is a rule, not a re-tag. Pin a launch, boost by margin, bury what is out of stock, and rank on the signals you choose, and the filters build themselves from real attributes.
- Pin, boost, and bury any product
- Rank by margin or stock
- Auto-generated filters and facets
- No relevance rules to write or maintain
How it reads your catalog
What goes in, what comes out.
The same idea, applied to real examples, where the left column is what you or a shopper provides and the right is the structured data an agent matches on.
cozy oversized knit for winter
waterproof hiking boots, wide, size 11
fragrance-free moisturizer for sensitive skin
gift for a 5-year-old who loves dinosaurs
The problem
What this fixes.
Your search box matches keywords, so 'something for a beach wedding' returns nothing useful while the right product sits unfound. Shoppers now expect to describe what they want the way they would to a person, and a keyword index cannot answer that.
How it works
Three steps, no re-platforming.
Run on a readable catalog
AI Search sits on the structured attributes Catalog Genius wrote, so filters and facets generate themselves from real attributes rather than from rules you maintain.
Answer real intent
Shoppers type the way they talk, and Discovery Prompts guide vague queries to the right product instead of returning nothing or the wrong thing.
Merchandise on top
You can pin, boost, or bury products and rank by margin or stock, so you control the result without rewriting the catalog underneath it.
What you get
The outcome, not just the feature.
Found by what they mean
Natural-language queries resolve to the right product instead of a wall of results or a dead end.
Filters that build themselves
Facets come from the real attributes in your catalog, so there are no rules to write or maintain.
You control what ranks
Pin, boost, bury, and rank by margin or stock, without rewriting the catalog underneath.
The proof
What it is worth.
NZ$624K
search-driven revenue, Collective Shoes
2.2x
repeat purchase from search shoppers
44%
higher lifetime value
AI Search turned Collective Shoes' search bar into their second-biggest revenue channel, NZ$624K in search-driven revenue.
What it doesn't do
- There are no relevance rules or tagging logic for you to write.
- No developer is needed to install or maintain it.
- It is not keyword search with AI on top, since the intelligence comes from the catalog it reads.
Questions merchants ask
How is this different from a $29 search app?+
A search app makes keyword search faster, while AI Search reads a catalog that has been mapped to structured attributes, so it answers natural-language queries and builds filters with no rules. What changes the outcome is the catalog underneath, not the search box on top.
Do I need to set up filters?+
No. Filters come from the structured attributes in your metafields, so a clean catalog gives you automatic facets.
Can I control ranking?+
Yes. Pin, boost, bury individual products, and rank results by margin or stock when you want to.
What are Discovery Prompts?+
Guided suggestions that turn a vague query into a specific one. They help a shopper who does not know the exact term reach the right product.
Get started
See what an agent reads when it hits your store.
Drop your store URL for a free readability score, or add the app and Atomz starts making the catalog readable today.