Get your products cited in AI shopping

The unit changes. A normal GEO audit scores pages against the questions your buyers ask, which works when you have dozens of pages that each carry an argument. A catalog has thousands of items where the thing competing for a citation is a single product, and where the buying query is phrased by a shopper, not by a researcher. So the module suggests keywords from the catalog itself, queries the engines the way a shopper would, and reports at product level: which items are recommended, which never appear, and how that moves over time. The audit page shows the GEO Score with its reasoning, the share of voice against competing products, the citations broken down by engine, and the trend.

Shopping engines do not only read your product pages, they read structured product data. The Merchant loop closes the gap between what the audit finds and where the engines look, so a corrected specification or a clarified attribute reaches the data layer rather than sitting in a report. In practice that means an issue surfaced by the audit can be pushed back rather than manually retyped into another tool. It also means the re-audit measures something real: if a description was rewritten and the structured data updated, the next run tells you whether the engines changed their answer.

Yes. The import is designed platform first rather than Shopify first: detection runs live, the native API is used when the platform exposes one, and the generic scraper covers the rest. The audit, the per-product scoring and the Merchant loop do not depend on which platform you sell on. What does vary is how the fix ships. On a platform with a native connector, an optimized description can be pushed back directly. Elsewhere the rewrite comes out of Citeme and goes into your own publishing flow. The measurement half is identical either way, which is the half that tells you whether any of it worked.

Because they answer from what they retrieved, and what they retrieved may be a cached page, an aggregator, or a marketplace listing rather than your store. An engine that reports an outdated price or a product as unavailable does more damage than one that ignores you, since the buyer acts on it without ever visiting your site. This is why the audit checks what the engines say about your products rather than only whether they mention them. Contradictions between the answer and your actual catalog are the fastest thing to fix and the most expensive to leave alone, and they are usually a structured data problem rather than a content problem.

The import is not the constraint, the querying is. Every buying query has to be run against the engines repeatedly to produce a trend rather than a snapshot, and that has a real cost per run, which is why volumes sit inside your plan rather than being unlimited. The practical approach is to prioritize instead of covering everything. A catalog usually has a small set of products that carry the margin and a long tail that does not justify recurring measurement. Start with the products you would defend in a meeting, get the trend on those, and widen once you know what a point of share of voice is worth to you.