We publish our own AI legibility results and update them on a schedule. We eat our own cooking, in public, on purpose, because the only honest way to sell a measure of how legible a store is to AI is to put your own store on the same scale first, and keep it there where anyone can check.
- We publish our own AI legibility results and update them on a schedule. We eat our own cooking, in public, on purpose.
- We go first because the only honest claim about legibility is one you keep re-checking out loud.
- On a schedule, because drift is real: a single scan is a snapshot, not a certificate.
- You should watch. Judge the method by how our own score moves, not by marketing.
There is a version of this business that lectures merchants about AI legibility while quietly hoping nobody scans the lecturer. That is not the version we are building. If we are going to tell you that your store has gaps a machine sees, the least we can do is show you ours, measured the same way, published openly, and kept current rather than frozen at a flattering moment.
Why go first, in public
Credibility in this category is not claimed, it is demonstrated. A score you cannot see is marketing; a score you can check is evidence. Putting our own results on the table, on our Our Own Score page, is how we prove the measure is real, the standard applies to us too, and we are not asking anyone to do something we have not done.
Why on a schedule
Because visibility drifts. A single published number frozen in time would rot like any other stale page. Re-scanning and re-publishing on a cadence keeps it honest and doubles as a working demonstration of the freshness discipline we ask of everyone else, and it feeds the anonymized benchmark we publish about Shopify legibility overall.
| What we do | Why it matters |
|---|---|
| Scan our own store, openly | The method is accountable, not just marketed |
| Re-run it on a schedule | Because legibility drifts and must be re-checked |
| Publish the score, good or bad | You can watch it move and judge us by it |
Why you should watch
Partly to hold us to our own standard, which is fair. Partly because a store that is improving its own legibility in the open is a useful thing to learn from. Either way, transparency is the point. It is how trust gets earned rather than asserted, which is the whole of being trusted by AI.
A scan is a snapshot. Legibility drifts
A public, repeated scan is never settled. A theme update rewrites your structured data, an app changes a tag, a redesign orphans a page, and the layer that proves who you are regresses silently while the storefront still looks perfect to you. Your store and the web around it change weekly, so a trusted, consistent entity is a moving target, not a one-time pass. That is why serious stores measure, fix, and re-measure, and why we measure, fix, and re-measure on a public schedule.
Questions people actually ask
Why does Visibility Mesh scan its own store?
Because credibility in this category is demonstrated, not claimed. If we measure how legible a store is to AI, the honest move is to put our own store on the same scale, publish the results openly, and keep them current where anyone can check them.
Where can I see your own score?
We publish it on our Our Own Score page and update it on a schedule rather than freezing it at a flattering moment. The point is that it is checkable, so the standard we hold others to visibly applies to us as well.
Why update your own results regularly?
Because AI visibility drifts as themes update and apps change, so a number frozen in time would go stale. Re-scanning and re-publishing on a cadence keeps it honest and demonstrates the same freshness discipline we ask of other stores.
See what a machine sees
You can't tell from your browser whether AI can read your store. You can find out in a few minutes. Run a free scan and see the exact layer the machine reads, and where you're losing the shortlist.
Sources: 2026 industry compilations on AI-search visibility; OpenAI (early 2026) on ChatGPT usage and shopping queries; Adobe Analytics (2026) on AI retail traffic; Gartner (2024) on traditional search. Figures are third-party and current as of mid-2026; we publish our own benchmark data as our scan volume grows.