The signals that decide whether AI recommends your product

A shopper asks an AI engine for a noise-cancelling headset recommendation under 200 euros. The engine isn't picking at random from whatever it has read. It's choosing, from everything available on the topic, whichever answer best fits the question with the most confidence behind it.

That choice isn't a coin flip, but it isn't an ad slot you can buy either. It rests on signals that can be identified, even if the exact selection mechanism stays specific to each engine.

What AI engines look at before recommending a product

Broadly, four families of signals keep showing up in what separates a product that becomes THE recommendation from one that's merely mentioned in passing: how clear and complete its listing is, whether third-party sources back it up, how consistent the information is everywhere it appears, and how directly it matches the intent behind the question.

• A product page that actually answers the question asked (use case, compatibility, material, size) rather than generic marketing copy
• Structured product data that's as readable by a machine as by a human shopper
• Customer reviews and third-party mentions that confirm what the listing claims
• Consistent information across sources: catalog, reviews, press, comparison sites
• No contradiction between what your store says and what's said about the product elsewhere

Brand visibility and product visibility are two different things

A brand can be widely known to a generative AI without any single one of its products ever getting recommended by name. The two get measured differently: brand visibility captures general mentions, while product visibility looks at whether a specific item, with its name, specs and price, is the one an AI cites in response to a concrete need. For a catalog with hundreds of SKUs, it's this second level that determines whether traffic and sales actually follow.

What isn't enough

Ranking well on Google guarantees nothing on the generative AI side. The two overlap partially, but an answer engine can easily skip a well-ranked page if it doesn't clearly answer the question asked, and cite a source you don't control instead of your own product listing. A listing can be perfectly optimized for classic search and still be absent from ChatGPT's answers, simply because the engine found a description elsewhere, on a third-party site, that it judged more complete or more trustworthy for answering the question.

Figuring out which of these signals is missing from your catalog, product by product, is exactly where MAP meets FIX at Tadow: measuring what an AI engine currently retains about each listing, then fixing whatever is keeping it from becoming the recommendation.