How to get your store recommended by AI shopping agents
A practical, no-fluff guide to getting recommended when shoppers ask ChatGPT, Perplexity, Claude or Google AI to find a product. What agents read, what to fix first, and how to check where you stand.
When someone asks ChatGPT, Perplexity, Claude or Google's AI to find them a product, software shops on their behalf. To get your store recommended by AI shopping agents, make the facts machine-readable: add Product schema (JSON-LD) with price, availability and ratings, keep the price in the page's HTML, state shipping and returns in plain text, and add an llms.txt. Agents recommend the stores they can read and verify, not the prettiest ones.
Most store owners miss this part. A shopper looks at your photography and reads your reviews. An agent does neither. It fetches the page, parses the HTML, and checks three things: what the product is, what it costs, and whether someone can buy it. Get those into the markup and you are in the running. Leave them buried in your design and the agent goes with a store it can read.
How do AI shopping agents decide what to recommend?
An agent works in one fast pass. Give it a request like "find me a midweight merino sweater under $150 with good reviews" and it goes hunting for structured facts it can match:
- What the product is. A clear name and description it can match to the request.
- What it costs. A price in the HTML, ideally inside Product schema, not locked in an image or rendered late by a script.
- Whether it can be bought. Availability, shipping and returns, stated in text.
- Whether you can be trusted. HTTPS, a real site identity, and ratings it can read.
State all of that cleanly and the agent weighs you on the merits. Bury it and you can lose to a worse product that happened to be readable. The bar sits higher than it does for a human visitor: an agent will not infer what you left out, and a missing price is enough to drop you from the comparison.
The signals that decide it, and where stores fall short
Most stores are closer than they think. Usually one or two specific signals are missing, and an agent treats those as disqualifying. The table below maps what agents read, why it matters, and the mistake that gets a store skipped:
| Signal | What the agent needs | Where stores get skipped |
|---|---|---|
| Product schema (JSON-LD) | Price, availability and rating in a trusted, structured format | No schema at all, so the agent has to guess and often picks a competitor |
| Machine-readable price | The price present in the HTML | Price only inside a product image or rendered late by JavaScript |
| Availability | A clear in-stock or out-of-stock state | No stated availability, so the agent cannot confirm the item is buyable |
| Title and description | Text that matches what the shopper asked for | Vague or marketing-only copy with no concrete product details |
| Ratings and reviews | Structured aggregate rating | Reviews shown only as star images with no readable numbers |
| Trust signals | HTTPS, real identity, visible policies | Missing returns or shipping info an agent can read |
| Agent directives (llms.txt) | A short file pointing agents to your best pages | No llms.txt, so the agent has to infer everything from raw pages |
Our own scans show the same thing. The average store we grade lands around 57 out of 100, and in a small sample of apparel stores, 8 in 10 had no Product schema on the page an agent reads. That is a limited sample, not a market census. Even so, it matches what we keep seeing: the basics go missing far more often than owners expect.
What to fix first
No rebuild required. Work in this order, since each step removes a reason for the agent to skip you:
- Add Product schema (JSON-LD) with price, availability and rating on every product page. This is the single biggest lever.
- Make sure the price lives in the HTML, not only in an image or a late-rendering script. Test it by viewing the page source and searching for the number.
- State availability, shipping and returns in plain, readable text.
- Add ratings as structured data, not just star graphics.
- Add an llms.txt to guide agents to your best pages once the basics are solid.
Do the first three and you have closed the gap that skips most stores. The rest is upside.
Why honest, specific content gets cited (and stuffing does not)
You might be tempted to game this by stuffing keywords or padding pages. Skip it. A Princeton-led study on generative engine optimization (Aggarwal et al., presented at KDD 2024) tested which tactics get a source surfaced in AI answers. Adding citations, quotations and real statistics raised visibility, sometimes by a wide margin for lower-ranked sources. Keyword stuffing ranked among the weakest tactics and lowered visibility in their tests.
So write for the person reading. State real numbers, cite where they came from, and put the product facts in the markup. Specific, checkable content is what gets cited. It also happens to be what sells.
How to check where your store stands
Before you change anything, see what an agent gets when it reads your store today. The fastest way to know where your store stands is to run the free ShelfGrader scan: paste your URL and you get a grade, the fixes ranked by impact, and the competitor an agent would pick instead.
From there, fix it yourself or hand it off. The $20 DIY fix pack generates your Product schema and llms.txt for you to paste in; the $99/month managed plan keeps them correct as your prices and products change. The grade itself costs nothing, so start there.
Frequently asked questions
How do AI shopping agents pick which store to recommend?
They read your page's HTML, not its design, and look for facts they can verify: a machine-readable price, product name and description, availability, and ratings, usually via Product schema (JSON-LD). When a store is missing those, the agent cannot confirm the details, so it recommends a competitor it can read cleanly.
Do I need Product schema to be recommended by ChatGPT or Perplexity?
It is the single biggest lever. Product schema (JSON-LD) states your price, availability and rating in a format agents trust. Without it, an agent has to guess what you sell and for how much, and it often guesses wrong or skips you. You can rank in some cases without it, but you are making the agent work harder and competing at a disadvantage.
How long does it take to start showing up in AI answers?
Plan in weeks, not days. Agents and search engines recrawl on their own schedule, so a fix you ship today may take days to weeks to be reflected in answers. The technical changes are fast to make; the visibility they earn compounds over time.
Is this the same as SEO?
It overlaps heavily. Product schema, machine-readable pricing and crawlable content help you in Google search and AI Overviews as well as with shopping agents, so the work pays off in both places. The difference is that agents are stricter: a human will forgive a price buried in an image, but an agent that cannot read the price will often filter you out entirely.
Does keyword stuffing help me get recommended by AI?
No, and it can hurt. A Princeton-led study on generative engine optimization (Aggarwal et al., presented at KDD 2024) found that adding citations, quotations and statistics raised a source's visibility in AI-generated answers, while keyword stuffing was among the least effective tactics and reduced visibility in their tests. Write clearly for people and structure the facts for machines.
How can I check if my store is visible to AI shopping agents?
Run your store through a tool that reads your live page the way an agent does and reports which signals are present or missing. ShelfGrader does this free in about a minute: paste your URL and you get a grade, the ranked fixes, and the competitor an agent would pick instead.