How do AI assistants decide which products to recommend?
They assemble an answer from sources they can read, classify, and trust — so being legible and corroborated matters more than ranking first.
Modern assistants answer recommendation questions in one of two ways: from what the model absorbed during training, or by retrieving live pages and summarising them. Most now do both — a base impression from training, refreshed with search results at answer time.
In both paths the same filter applies. The system needs to know what category you belong to, be able to state what you do without hedging, and find some corroboration outside your own marketing. A page that satisfies all three is safe to cite. A page that satisfies none is invisible even if it ranks well on Google.
That is why traditional SEO and AI visibility diverge. Ranking is about matching a query to a page. Recommendation is about whether a model can confidently describe you to a stranger. You can rank first and still never be named.
Practically: make the category explicit, publish answers to the questions buyers actually ask, and give the model something verifiable to lean on. Those three moves cover most of the gap.
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