Retailers are rushing to make their product listings discoverable by AI shopping agents, but AI assistants often struggle to determine when different listings describe the same item. To address this problem, Shopify has launched a new system called Catalog that uses LLMs to organize merchant product data into a format agents can use to identify and compare products.

In a blog post published this month, Shopify described two merchants selling the same protein powder. One might create a single product listing with multiple flavor variations, while another creates separate listings for each flavor. Catalog uses AI to group related listings.

“This is a tale of teaching machines to read product data the way a human shopper would.”

“This is a tale of teaching machines to read product data the way a human shopper would,” Mariya Mansurova, a staff product data scientist at Shopify, writes.

The launch comes as product discovery moves from organic searches and storefront scrolling to AI agents performing those tasks on a user’s behalf. Shopify’s data backs this up: AI-driven traffic to its stores grew eight times year over year in the first quarter of 2026, while orders from AI-powered searches increased nearly 13-fold. New buyers are coming through AI channels at nearly twice the rate of other channels. That change is forcing retailers to rethink how product information is organized and delivered to agents.

AJ Ghergich, senior vice president of AI and global services at Botify, a search and AI visibility platform, tells The New Stack that AI-driven product discovery has become a focus for many of his retail clients. Catalog is Shopify’s response to this bigger challenge facing retailers.

“The thing I can’t get off the phone [with clients] about right now is some version of an agentic catalog…”

“The thing I can’t get off the phone [with clients] about right now is some version of an agentic catalog, whether it’s Shopify, or whether it’s something they’re building themselves, partnering with Botify,” he said. “Right now, a lot of your information is pulled from a crawl. All of the forward-thinking brands are thinking about how they can push that information into the AI and feed the most logical way to start.”

Botify plans to launch its own agentic catalog product and is currently piloting it with several large retail brands, according to Ghergich.

Toward common standards

Beyond tools like Catalog, retailers and technology companies are working on standards that make it easier for AI agents to interact with merchant product listings.

Ghergich points to the Universal Commerce Protocol (UCP), an open standard launched earlier this year by Shopify and Google that standardizes how AI agents interact with merchants, from creating a cart to completing payment. Backers include Etsy, Target, Walmart, and Wayfair.

“I’m seeing a lot of early adoption, and I see a lot of the folks who are planning for their back end to speak to that protocol,” he says.

Retailers are also rethinking how they present product information in online stores. Since many AI agents retrieve information directly from websites, brands are experimenting with markdown and other formats that make product information easier for AI agents to read, he adds.

How Catalog works

Catalog groups related listings under a Universal Product Identifier, which helps AI agents recognize when different listings refer to the same underlying product.

To figure out whether products belong together, Shopify’s system determines what it calls a product’s “core value proposition,” or the main reason a customer is buying it. For protein powder, shoppers usually buy it for its nutritional value rather than a specific flavor, so chocolate and vanilla versions may be grouped together. 

“A missed grouping may make a product harder to discover, but an incorrect grouping could cause an AI agent to recommend the wrong item.”

The system helps AI agents differentiate among variants of the same product and among different products altogether. Shopify said it takes a “precision-first” approach. This means it prioritizes avoiding incorrect product groupings over catching every possible match. A missed grouping may make a product harder to discover, but an incorrect grouping could cause an AI agent to recommend the wrong item.

“When we get this right, merchants’ products show up exactly where they should across every agent, which helps them get customers from new agentic distribution channels,” Shopify writes.

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