Is your store visible to ChatGPT? How to check (and get recommended)
When ChatGPT, Gemini or Perplexity answer "where can I buy X," they read the machine-readable version of your product page. Here is how to check whether yours is visible, in about 60 seconds.
Short answer: probably less than you think, and you can check it in about 60 seconds. When ChatGPT, Gemini, Perplexity, or Google's AI Overviews answer "where can I buy X," they read the raw, machine-readable version of your product page, not the pretty one a shopper sees. If your prices, stock, and product details are not in a format an agent can parse, you are invisible to that shopper. Paste your URL into ShelfGrader for a free grade.
The proof (first-party): ShelfGrader scores any store URL against a 10-signal rubric out of 100, in about 60 seconds, no signup. Across the stores scanned so far the average grade is about 57 out of 100. That is a limited sample, stated honestly, not a market census, but it is enough to say most stores have real gaps. In a small sample of apparel stores, about 8 in 10 had no Product schema on the page an agent reads, which is the single fastest way to go invisible.
What "visible to ChatGPT" actually means
An AI shopping agent does not browse like a person. It fetches your page, looks for structured, machine-readable facts, and uses those to decide whether to recommend you. A human can read "$49, in stock" off a styled button. An agent often cannot, unless that same fact is written in a format it parses, like Product schema (JSON-LD) or a clean, readable price in the markup.
So "visible to ChatGPT" is not about ranking on Google. It is about whether the agent can extract your price, availability, title, description, images, and reviews without guessing. If it has to guess, it usually skips you for a competitor whose facts are plain.
How to check in about 60 seconds
The fastest check is to grade the live page the way an agent reads it. ShelfGrader does exactly that: paste a product or store URL, and it returns a letter grade out of 100 plus the specific fixes, ranked. No account, no waiting.
It scores 10 signals. Here is the full rubric and what each one is worth:
| Signal | Points | What an agent is looking for |
|---|---|---|
| Product schema (JSON-LD) | 16 | Structured product facts it can parse directly |
| Machine-readable price | 12 | A price it can read without scraping a styled button |
| Title + meta | 8 | A clear, accurate product title and meta description |
| Description depth | 8 | Enough real detail to match a shopper's question |
| Images + alt text | 8 | Images it can identify and describe |
| Reviews + ratings | 10 | Social proof in a form it can extract |
| Availability + shipping | 10 | In-stock status and shipping facts, machine-readable |
| Trust signals | 8 | Returns, contact, policies it can verify |
| Agent directives (llms.txt) | 10 | A file that tells agents what to read and how |
| Crawlability | 10 | Whether the agent can reach the page at all |
The two heaviest single fixes are Product schema (16 points) and machine-readable price (12). Together they are more than a quarter of the grade, and they are usually the cheapest to fix.
The most common reason stores fail: no Product schema
Product schema is the JSON-LD block that spells out your product as data: name, price, currency, availability, rating. It is invisible to shoppers and essential to agents. In the small apparel sample above, about 8 in 10 stores had none on the agent-read page (a limited sample, not a census), which means the agent had to guess every fact or move on.
If you only fix one thing, fix this. It is worth 16 of the 100 points and it is the difference between an agent quoting your price and an agent skipping you.
How to get your products recommended by ChatGPT
Being visible is step one. Getting recommended is the goal, and it comes from the same place: giving the agent clean, complete facts it can quote with confidence. An agent recommends the store whose price, stock, rating, and product details it can read without guessing. When two stores sell the same thing and one has structured data and the other does not, the readable one wins the mention.
In practice, getting recommended means doing well on the signals that carry the most weight:
- Complete Product schema (16 points). This is the data block an agent quotes from. No schema, no clean quote, no recommendation.
- Machine-readable price (12 points). A price the agent can read as a number, not a styled button it has to scrape.
- Reviews and ratings as data (10 points). Social proof the agent can extract tips a close call in your favour.
- Availability and shipping (10 points). An agent will not recommend a product it cannot confirm is in stock.
- An llms.txt file (10 points). It tells agents what to read and how, which removes guesswork.
Visible means the agent can find you. Recommended means it trusts your facts enough to put your name in the answer. The rubric above scores both, so the fastest way to move from one to the other is to grade the live page and fix the heaviest gaps first.
What actually moves the needle (third-party evidence)
This is not just our opinion. The Princeton-led GEO study (Aggarwal et al., KDD 2024) tested what changes a page's visibility in AI answers. It found that adding citations, quotations, and statistics raised visibility, while keyword stuffing was among the least effective tactics and actually reduced it.
The takeaway for a store: structured, verifiable facts win, and the old SEO habit of stuffing keywords backfires with agents. The 10-signal rubric above is built around facts an agent can extract, which is the same thing the study rewarded.
Once you have your grade, here is the fix path
| Your situation | What to do | Cost |
|---|---|---|
| Just want to see the gaps | Run the free grade, read the ranked fixes | Free, no signup |
| Comfortable editing your store, want it done right | DIY fix pack: Product schema JSON-LD, llms.txt, a guide, and a re-grade | $20 one-time |
| Want it done for you, kept current | Managed: done-for-you fixes with a monthly re-grade | $99/mo |
Most owners start with the free grade, fix the two heavy signals, and re-check. There is no signup to see your score.
Wondering where you stand? Paste your store URL for a free grade, about 60 seconds, no signup. Read how to check your AI visibility or how to get recommended by AI agents first.
Frequently asked questions
How do I check if my store shows up in ChatGPT shopping?
Grade the live page the way an agent reads it. ShelfGrader fetches your URL, checks 10 signals an AI shopping agent uses, and returns a grade out of 100 plus ranked fixes in about 60 seconds with no signup. The most common failure is having no Product schema on the page the agent reads, which means it cannot extract your price or stock as data.
Why is my store invisible to ChatGPT?
Because agents read the machine-readable version of your page, not the styled one. If your price, availability, and product details are not in a parsable format like Product schema (JSON-LD), the agent has to guess and usually skips you. Across the stores scanned so far the average grade is about 57 out of 100, which is a limited sample stated honestly, not a market census, but it points to most stores having real gaps.
How do I get my products recommended by ChatGPT?
Give the agent clean facts it can quote. Recommendations go to the store whose price, stock, ratings, and product details are readable as data, so the heavy signals matter most: complete Product schema (16 points), machine-readable price (12 points), reviews and ratings as data (10 points), availability and shipping (10 points), and an llms.txt file (10 points). Grade the live page and fix the biggest gaps first.
Do Shopify or WooCommerce stores need to apply to appear in ChatGPT?
No. There is no application or approval step. An agent reads your live product page and recommends you if it can extract your facts as data. The work is making your price, schema, stock, and reviews machine-readable, not signing up for a list. In a small apparel sample, about 8 in 10 stores had no Product schema on the agent-read page (a limited sample, not a census), which is the usual reason a store does not appear.
How does ChatGPT decide which products to recommend?
It reads the machine-readable version of a page and recommends products whose facts it can extract and trust: a parsable price, in-stock availability, ratings, and a complete Product schema block. The Princeton-led GEO study (Aggarwal et al., KDD 2024) found that structured, verifiable facts raise visibility in AI answers while keyword stuffing reduces it, so clean data beats stuffed titles.