What is AI product discovery?

AI product discovery is not Google Discover, the news carousel on your phone, and it is not a site search with AI for your own website. It is what happens when someone asks an assistant for "waterproof hiking boots for wide feet" and the assistant has to know, before recommending anything, which products exist that answer that. It is the first stage of agentic commerce, and the one that depends most on the store: an AI agent does not recommend what it has not found, and what it finds of yours is what your data says, not what your storefront shows.

How it works

An agent does not walk through stores one by one when it gets the question. It works from what it already has or can query, and that reaches it three ways.

Feeds the store sends. OpenAI defines a product feed so ChatGPT can accurately show a merchant's products in ChatGPT search and shopping: a file the store updates regularly, with identifiers, descriptions, prices, stock, images and delivery options.⁠^{[1]OpenAI Developers. Key concepts (Agentic Commerce)} Its specification accepts JSONL, CSV or TSV, requires nine fields and separates two permissions: search is on by default and checkout inside ChatGPT is off by default. It also warns that being enabled does not guarantee the product will be shown.⁠^{[2]OpenAI Developers. Products (feed specification, Agentic Commerce)} The protocol behind it has its own entry, ACP (Agentic Commerce Protocol). On Google, the equivalent route is Google Merchant Center: the first step to sell from Google AI Mode and Gemini is to have the account set up with the feed, shipping and returns.⁠^{[3]Google for Developers. UCP on Google: integration guide overview}

Catalogs platforms expose. Here it is the agent that asks. Shopify Catalog offers two interfaces for agents to discover products: the global catalog, which searches across all the Shopify stores that meet the requirements, and a single-store one. Both expose the same tools (search_catalog to search by keywords, lookup_catalog to look up by identifier and get_product for the detail) and return price, variant availability and checkout links. They run on MCP (Model Context Protocol) as the catalog capability of UCP (Universal Commerce Protocol).⁠^{[4]Shopify, developer documentation. Shopify Catalog (agents)} The agent uses them through tool calling.

Pages that get crawled and indexed. Google gives two ways to share product data: structured data on product pages and uploading to Merchant Center, and it recommends using both.⁠^{[5]Google Search Central. Share your product data with Google} What a crawler reads on your pages is still raw material for discovery.

From catalog to answer

Google explains how the pieces fit together in AI Mode. It combines Gemini with its Shopping Graph, which in May 2025 held more than 50 billion product listings, more than 2 billion of them refreshed every hour.⁠^{[6]Google, official blog (May 20, 2025). Shop with AI Mode, use AI to buy and try clothes on yourself virtually} Faced with a purchase request, it uses query fan-out: it runs several searches at once to understand the need, infers the requirements (in its example, a travel bag for Portland in May needs to be waterproof and have accessible pockets) and proposes products that meet them, in a panel that updates as the person refines.⁠^{[6]Google, official blog (May 20, 2025). Shop with AI Mode, use AI to buy and try clothes on yourself virtually}

That breakdown is what a store has to understand. The query arrives split into requirements, and each requirement is checked against a piece of data. If the data is not there, the product does not meet the requirement as far as the system is concerned, even if in the physical store it does.

In a store

A fictional example: a mountain gear store gets, through an assistant, the request "sleeping bag rated to 20°F that weighs under 2 pounds, for a small backpack." Split into requirements, each one needs a specific piece of data:

A sleeping bag that meets all four conditions and has the temperature only on the packaging image competes at a disadvantage against one that has it written down. That is the part Clione works on: from the enriched catalog it builds a feed that uploads to ChatGPT every day and a supplemental feed for Google Merchant Center; whether the products appear, and where, each platform decides.

How to check it

1. Write three purchase requests the way a customer would, without your brand and with specific requirements, and run them in ChatGPT and in AI Mode. Note which products and which stores show up, and on which requirement yours drop out.
2. Review your Google Merchant Center account: feed with no errors, shipping and returns set up.⁠^{[3]Google for Developers. UCP on Google: integration guide overview}
3. If you send a feed to OpenAI, validate every row: the nine required fields and an explicit availability.⁠^{[2]OpenAI Developers. Products (feed specification, Agentic Commerce)}
4. If you sell on Shopify, complete each product's data in your admin, so the catalog does not have to infer it.⁠^{[4]Shopify, developer documentation. Shopify Catalog (agents)}
5. Compare the requirements in your queries with your data. Every requirement without a written value, on the product page and in the feed, is a reason not to show up.

Related concept: AI visibility

AI visibility measures the outcome: whether your brand, your products or your pages appear in assistants' answers. Discovery is the mechanism before it: how your products come to be among those the system can show. You can have good visibility as a source of articles and not be discoverable as a store, because a cited page and a product with price, stock and attributes in a feed are different things. After discovery comes recommendation, and then the purchase, which a shopping agent can make and which brings in matters like coupons for agents.

To go further, the agentic commerce reading path orders the protocols that connect an agent to a store's catalog.