It is a customer who calls instead of walking in.
Think of a customer who does not visit the store: they phone. They ask the voltage, the installed price and whether it arrives by Friday. If the clerk cannot answer, they call the next store. They never saw the window, the banner or the promotion.
The AI assistant that shops for the customer (ChatGPT, Gemini, Meta's Muse) is that customer on the phone. It does not run the page like a browser: it downloads the HTML, looks for the spec sheet written for machines (structured data) and the product barcode, so it knows your air conditioner is the same one sold next door. If the price only appears after JavaScript runs, for the agent there is no price.
And there is the doorman. Many stores welcome AI bots on the sign on the door (the robots.txt) and then the firewall blocks those same bots on the third request. The store thinks it is open; the agent finds the door shut and leaves.
What we found at four large retailers.
Appliances, furniture and pharmacy. All with stores built well for people. We downloaded 22 to 24 product pages from each, without running JavaScript, the way AI bots do, and ran real shopping searches. For agents, it is a different story.
Store A · home appliances
Store B · appliances & furniture
Store C · pharmacy
Store D · pharmacy
Pilot sample, September 2026: 22 to 24 product pages per store, sampled from the sitemaps and downloaded without JavaScript. The shopping searches are an approximation (one web search engine, one run per question); measuring ChatGPT, Gemini, Perplexity and Muse is the next phase. The firewall test ran from a single address. Stores anonymized.
What the agent checks: seven points.
In our reading, each score comes with the address, the date and the snippet that proves it. Here you can do a quick self-check: tick what you think your store already has.
Agent checklist
It starts free and only becomes a project if the reading shows it pays off.
We have fixed e-commerce under pressure.
Two cases at one of Brazil's largest home-appliance e-commerces, and the next channel stores will have to serve.
Software engineering in Porto Alegre since 2004.
The people you talk to are the people who write the code, from diagnosis to production.
No "could be better": every item comes with the page, the date, the response code and the snippet.
The reading runs on our own code, with no third-party tool in the middle.
We guarantee what can be verified and measure the rest every month.
Agents that work for you.
With a clean catalog, the same data feeds agents that operate your store: they adjust prices within ranges you set, warn about stock-outs before they happen and log every decision.
Every action goes through an approval checkpoint you control. What is within range goes ahead; what is outside goes back to a person. How we govern agents in production.
What people always ask.
Is this SEO under another name?
No. SEO competes for a position in a search engine. Here the goal is for an agent to be able to read, compare and trust your catalog. Most of the work is verifiable engineering: the HTML, the structured data, the firewall.
Do you guarantee I will show up in ChatGPT?
Nobody can honestly guarantee that. We guarantee what can be verified: that the agent can get in, read and understand your store, and we measure every month where you show up.
My platform already integrates with Gemini. Do I need this?
The integration connects the pipe. If the catalog flowing through it has no barcode, hides the price or keeps the spec sheet only in JavaScript, the agent gets little and picks another store.
What happens to my store's data?
The reading only touches public pages. Any deeper test, such as the firewall one, is only done with written authorization.
See your store the way the agent does.
You get by email what an agent can read from your store, with the proof for each item. Public pages only; our bot identifies itself as Stickybit and respects robots.txt.
The product number that is the same in every store. It is how the agent knows two listings are the same air conditioner. A store's internal code in its place is worse than empty. More
Structured data: name, price, stock, voltage and rating written in a format the agent reads directly, without guessing from text. More
The site firewall. It can block AI bots even when the sign on the door (robots.txt) says they may come in. More
Where this could be wrong.
Small sample
22 to 24 pages per store, at four stores. Enough to find defects repeated on 100% of pages (like the barcode), not to grade an entire store.
Approximate search
Shopping searches ran on one web search engine, once each. Real ChatGPT, Gemini, Perplexity and Muse answers vary between runs; measuring that is the full diagnosis.
The doorman seen from one address
We tested the firewall with the bots' names, from our address. A firewall that checks the bot's real address would be right to block a fake "GPTBot"; only the store's logs confirm.
Nobody guarantees showing up
2026 studies show no technique has a stable effect on being discovered by AIs. What is solid: being readable, having data only you have, and measuring every month.
- Our own reading of four large retailers (appliances, furniture, pharmacy), September 2026: 22 to 24 product pages per store, without JavaScript; shopping searches as an approximation. Stores anonymized.
- 2026 studies on citation in AI answers (arXiv 2606.20065; critical survey arXiv 2607.14035).
- WebContinental cases (2019 and 2020): Stickybit projects.