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Sector · Ecommerce

AI visibility for
ecommerce brands

Shopping questions are now answered in prose: what to buy, which material lasts, which brand fits a particular need. Those answers are assembled from pages a system can actually read — and on a large share of ecommerce sites the specifications, materials, sizing and provenance a shopper asks about only exist after the page finishes rendering. The page ranks. The retrievable version of it is close to empty. OG01 measures the difference.

Score your store free → The shell problem
The buying problem

Ranking and retrievability have quietly separated

A modern storefront defers almost everything. Reviews load from a third-party widget. Specifications sit behind a tab. Shipping, returns and materials are in an accordion. Variant data arrives with the price. All of it is present for a shopper and much of it is absent from the document a retrieval request receives, because that request does not click, scroll or wait.

The result is a category where the pages that perform best commercially are frequently the least useful to an answer engine. Nothing in a conventional report shows this, because rankings are computed against an index that was built with a great deal more patience than a live retrieval has.

The second half of the problem is corroboration. Product claims — organic, handmade, Australian-made, hypoallergenic, lifetime warranty — typically originate and terminate on the brand's own site. A system that cannot verify a claim usually declines to repeat it, so the brand with the strongest differentiation can be the one least often described.

What gets measured

Read as a machine, not as a browser

Served-HTML substance
What is actually in the document before scripts run: how much of the product story survives, and which categories of detail vanish entirely.
Product structured data
Whether machine-readable product information exists, is valid, and agrees with the visible page rather than contradicting it — a surprisingly common mismatch after a theme change.
Claim corroboration
Which product and brand claims are supported anywhere other than your own store, and which are load-bearing for your positioning but entirely self-published.
Marketplace collision
Where your own products are described by marketplaces and resellers in terms that differ from yours, and which description a system is more likely to trust.
Who buys it

The person accountable for a channel that stopped growing

This is usually a head of ecommerce or a performance marketer, and the trigger is almost always the same: paid acquisition costs keep rising, organic is flat, and nobody can explain where the incremental demand went. An external reading is useful precisely because it does not come from the agency running the paid account or the developer who built the theme.

It is also frequently bought to settle an internal argument about a replatform. A reading taken before and after a theme or platform change is one of the few ways to demonstrate that a rebuild helped or hurt something other than page speed.

Fix and prove

Server-render the substance, then corroborate the claims

  • Move the answer into the document. Specifications, materials, sizing and care detail belong in the served HTML. This is usually a theme or template change rather than new content, which makes it cheap relative to its impact.
  • Make structured data agree with the page. Not merely present — consistent. A product marked up with a price the page no longer shows damages confidence rather than building it.
  • Corroborate the claims that matter. Certifications, testing, materials provenance: get them stated somewhere that is not you. This is the slowest item and the one that most changes how you are described.
  • Reconcile marketplace descriptions. Where resellers describe your products differently, decide which version is correct and correct the others.

Rescore after a catalogue-wide change, not after one product. Ecommerce remediation is usually applied at template level, so the honest measurement point is once the change has propagated across the catalogue and been recrawled. Seasonal stores should also avoid rescoring across a peak, since the assortment itself changes what is there to read.

SaaS and software → What the reading contains → What remediation involves → The AI Readiness card → Pricing →
Questions

Ecommerce questions

Our store is on a hosted platform. Can we even fix the rendering?
Usually more than you would expect, because most of what matters is theme-level rather than platform-level: which content the template outputs directly versus which it defers to a script. Where something genuinely cannot be changed, the finding stays on the roadmap as unresolved rather than being quietly dropped, so a later rescore explains itself.
Do you score individual product pages or the whole store?
The reading is per domain and samples the site rather than crawling every product. Where coverage is partial it is reported as a ratio rather than a raw count, because in a large catalogue an unqualified page count would badly overstate how much was actually read.
Will this help us appear in AI shopping results?
It measures and improves the conditions that make a product page usable as a source, which is the part you control. Whether any particular shopping surface then features you is that system's decision, and no measurement platform can promise it. What can be promised is that the same measurement runs again afterwards.
We sell on marketplaces as well. Does that compete with our own store?
Often, and the reading surfaces it. Marketplaces are heavily corroborated, so their description of your product can become the version systems trust. That is not automatically bad — but where their description disagrees with yours, you want to know which one is circulating.
How do product reviews affect this?
Third-party review content is a corroboration signal, and OG01 measures whether such corroboration exists. It does not publish ratings or review counts as claims of its own, and no OG01 output asserts a rating for your business — there is nothing verifiable behind a number like that coming from a measurement tool.
See what a machine sees on your product pages.

One URL, about ninety seconds, no card. The gap between what ranks and what is retrievable is usually the finding.

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