How we grade your store for AI shopping agents.
When someone asks ChatGPT or Gemini what to buy, the assistant answers from what it can read: your pages, the structured data inside them, and the feed you send to shopping platforms. This audit measures how much of your store an agent can actually use — reading only your public pages, the same access an agent has.
Agents recommend the stores they can read.
An AI assistant doesn’t browse your site the way a person does. It fetches the HTML, looks for machine-readable facts, and cross-checks against feeds and structured data. When those are present and correct, it can name your product, quote the right price, and put you in the answer. When they’re missing, it works with whatever store it could parse instead.
So we grade the store from an agent’s point of view, on evidence we can actually observe on your public pages — never your traffic, your admin, or anything behind a login. Every point in your score traces back to a specific signal we saw, or didn’t.
Five categories, weighted by how much they move an agent.
Dozens of individual checks roll up into five categories. Each owns a fixed share of the 0–100 score, set by how directly it changes whether an assistant can use your store. The shares add up to 100.
What your score actually means.
Every store we can read gets one number from 0 to 100, plus a one-word band. The number is a weighted blend of the five categories above. Higher means an agent can do more with your store with less guessing.
The full report also scores each assistant on its own — ChatGPT, Perplexity, Gemini, and agentic checkout read your store differently, so a store can be strong for one and weak for another. And it grades your individual products, so you can see exactly which ones are agent-ready and which are missing a price, an ID, or stock status.
We grade on what we can verify, and never invent the rest.
Every signal carries a confidence
A signal is detected when we saw it directly, such as a price sitting in your Product markup. It’s likely when strong indirect evidence points to it. It’s unknown when we honestly couldn’t check. An unknown never counts as a failure and never earns phantom credit. It just narrows how much of that category we can vouch for.
Coverage keeps a high score honest
A category’s score is bounded by how much of it we actually verified. If we confirmed only a sliver of the signals, it can’t read as a confident 100. A high number means broad, verified strength, not one lucky check standing in for the rest.
Categories combine by weight, not by average
The five category scores fold into the composite by the shares shown above. Because it’s weighted rather than averaged, a strong category can’t paper over a weak one, and a single missing signal drops the category it belongs to rather than capping your whole store.
First we prove it's actually a store.
Running on a commerce platform isn’t proof of a storefront. A marketing site can sit on Shopify; a SaaS billing page has “checkout” in its copy. We only put a number on a site when we find real product evidence. When we can’t, we say which of four things happened instead of forcing a score.
The formats agents actually read.
Public, named, and easy on your servers.
- Public pages only. No login, no admin, nothing behind an account. We see what any visitor or agent would.
- An honest crawler. We identify ourselves with a named user-agent and never impersonate a browser or a verified bot to slip past protections.
- Gentle on your origin. Per-host concurrency limits and backoff, so a scan won't storm your servers or trip your rate limiting.
- Confined to the open web. Requests only reach public internet addresses — never private, internal, or cloud-metadata endpoints.
What runs today, and what's next.
- Public-page fetching & parsing
- Product structured-data analysis
- Feed completeness & freshness
- Feed-to-page price parity
- robots.txt & AI-crawler access
- Bot-wall / WAF detection
- A JavaScript-rendering check, so we grade what an agent sees after your scripts run, not just the raw HTML
- A model-in-the-loop probe that asks a real AI to pull your product facts, testing whether the markup actually works
See where your store stands.
A full read takes about ten seconds and touches only your public pages.
Run my audit →