How AI assistants decide which brands to recommend

When someone asks an AI assistant for the best option in your category, the assistant doesn't consult a league table. Modern engines with web search run live queries, read a handful of pages, and synthesise an answer from what those pages say. Which means the real question behind every recommendation is simpler than it looks: which pages did the engine read, and who do those pages talk about?

Run enough scans and the same source types keep appearing in the citations. Review platforms, because they're structured, current and comparative. Community threads — Reddit especially — because engines treat unpolished peer experience as a strong authenticity signal. Editorial round-ups and comparison pages, because a 'best X for Y' article maps perfectly onto a 'best X for Y' question. And your own site, which tends to be cited for claims about you but rarely decides whether you get recommended against rivals.

The uncomfortable implication: your visibility lives mostly on pages you don't control. If the review sites in your category barely mention you, if the community threads recommend the same two competitors every time, the assistant is going to reproduce that consensus — however good your own content is. Assistants don't take your word for how good you are; they take the ecosystem's.

The practical response is outreach with a measurement layer. Find out which specific sources the engines cite for your buyers' questions — they differ by category and by engine — then earn presence there: reviews on the platforms that keep appearing, honest participation where your buyers ask for advice, and comparison content on your own site that's genuinely useful enough to cite.

A Skavora report gives you that target list directly: every source the AI drew on across your scan, ranked by influence, with your presence (or absence) on each marked. It turns 'be more visible to AI' into a set of named sites to go and win.