How to Make Your Products Visible to ChatGPT and Gemini

Why can't ChatGPT or Gemini see my products?

Because your website is built for people, and AI agents are not people.

A human visitor sees a photo, a price, a "Add to basket" button, and reads between the lines. An AI agent sees a wall of HTML, scripts, and marketing copy with no reliable structure telling it what the product is for. It cannot tell whether your chair is good for lower back pain, whether your drill goes through brick, or whether either is in stock today.

So the agent does the safe thing: it recommends products it can understand, usually competitors who have exposed their data cleanly, or big marketplaces. You are invisible not because your product is worse, but because it is unreadable.

How do ChatGPT and Gemini actually find products?

Modern assistants combine a few mechanisms:

• Retrieval over structured data: they pull in machine-readable data (feeds, structured markup, APIs) they can reason over reliably.
• Live browsing and tool use: some agents fetch current pages or call tools, but parsing a human page is slow, lossy, and often blocked.
• Intent matching: crucially, they match a shopper's question, problem, or use case to products, not just keywords.

The through-line: the cleaner and more intent-rich your data, the more confidently an agent will surface your product as the answer. Everything below is about giving them exactly that.

Step 1: Publish structured product data

Give agents machine-readable data instead of asking them to scrape a page. Each product needs a stable ID, title, category, image, and a direct URL to buy, exposed in a format built for machines rather than browsers.

Step 2: Map every product to intent

This is the step almost everyone misses. Agents match on the questions, problems, and use cases a product addresses. So spell them out:

{
  "product_id": "ergo-chair-lumbar-01",
  "title": "ErgoLift Pro Office Chair",
  "problems": [
    "Lower back pain from sitting all day",
    "Cheap chairs with no lumbar support",
    "Not enough room for a big chair in a small office"
  ],
  "questions": [
    "What is the best office chair for back pain?",
    "Which ergonomic chair suits a small home office?"
  ],
  "use_cases": [
    "10-hour work-from-home days",
    "Small home office setups"
  ],
  "merchant_url": "https://merchant.com/products/ergolift-pro"
}

Now when a shopper tells Gemini "I have back pain and work long days in a tiny office," the agent has an explicit match, not a keyword guess.

Step 3: Keep price and stock live

An agent that recommends an out-of-stock or mispriced product looks unreliable, so agents learn to avoid stale sources. Sync price and availability daily so your data stays trustworthy and keeps getting surfaced.

Step 4: Make your data machine-discoverable

Agents need to find the data before they can read it. Two signals do the heavy lifting:

• A <link rel="agenticfeed"> tag on your site pointing agents to your root feed.
• An MCP server so AI agents can query your catalogue and resolve products directly through a standard protocol.

Together they turn "hope the model finds you" into "the model has a documented door to walk through."

Step 5: Measure AI-driven traffic

Add UTM parameters to the URLs agents send buyers to, so AI-driven clicks and sales show up in Google Analytics or whatever you already use. You cannot improve what you cannot see, and this is how you prove the channel is working.

Do I need to do all this per model?

No, and this is the good news. If you publish through an open, standard structured feed, any compliant agent, ChatGPT, Gemini, Claude, Perplexity, and whatever launches next, can read the same data. You structure your catalogue once; it works everywhere. Optimising separately for each assistant is a treadmill you do not need to be on.

The shortcut: an agentic feed

Doing all five steps by hand across a full catalogue is a lot of work. That is exactly what Agenticfeed automates: it generates the intent data (questions, problems, use cases) for every product, keeps price and stock in sync, publishes the discovery feed and MCP server, and adds UTM attribution, from a one-click Shopify connect or a simple integration, in minutes.

You go from invisible to readable, recommendable, and buyable by AI agents, without rebuilding your site.

Frequently asked questions

Will good SEO make my products visible to ChatGPT and Gemini?

Not reliably. SEO optimises pages for ranking in search results. Agents reason over structured, intent-mapped data. You can rank well and still be invisible to an AI shopping agent. See our guide on Answer Engine Optimization.

Is a Google Merchant Center feed enough?

It helps with structured basics like price and availability, but it was built for shopping ads, not for the questions, problems, and use cases AI agents reason over. An agentic feed adds that intent layer.

How long does it take to become visible?

Setup can take minutes with a one-click connection. From there, as agents discover and read your feed, your products become eligible to be recommended wherever shoppers are asking.

The takeaway

ChatGPT and Gemini are not ignoring your products out of preference, they simply cannot read them. Give agents structured, intent-mapped, live data with a clear way in, and you stop being invisible.

That is the entire job of Agenticfeed.