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A growing share of questions never reach a results page. I work on whether models can find, parse and quote you correctly — structured content, factual precision, and claims that survive being summarised by something with no reason to be generous.
People in your category are asking assistants instead of searching, and you have no idea whether you come up. When you do get mentioned, the description is out of date or belongs to a competitor. Or your traffic is falling while your rankings haven't moved, which is usually the first visible symptom.
The first step is measurement, because most opinions here are guesses. What do the major assistants actually say when asked the questions your buyers ask, and which sources are they drawing on.
Then the fixable part. Models cite what they can parse and trust: specific claims, clear structure, consistent facts across the pages that mention you, and third-party sources that corroborate. Vague positioning copy fails here for the same reason it fails with a human reader — there's nothing in it to quote.
Practically that means tightening the claims on your own pages, structuring content so an answer can be lifted cleanly, and working on the places outside your site that models draw from. It overlaps heavily with conventional SEO, and the overlap is the point — the same work serves both.
Two to three weeks for a baseline and recommendations. Re-measurement suits a monthly or quarterly rhythm, because the answers change as models update.
Can you actually influence what an AI says?
Not directly, and be wary of anyone claiming otherwise. You influence the sources it reads, which is slower and more durable.
Is this separate from SEO?
Related but not identical. The technical and content foundations are shared; the measurement and some of the tactics are different.
How do you measure it?
By running your buyers' real questions across the major assistants on a schedule and tracking what comes back, rather than relying on a single spot check.