Real tactics, no hacks, no guarantees — and an honest look at what's still out of your hands.
There is no reliable list of "AI ranking factors" the way there's a rough industry consensus on SEO ranking factors built from two decades of testing. Nobody outside the AI labs knows the exact mechanism a given model uses to decide which brand to name in a given answer, and that mechanism can shift with every model update. Anyone selling you a guaranteed way to "hack" ChatGPT into recommending you is overselling — the tactics below are the ones grounded in how these systems are known to actually work, and they're about improving your odds, not guaranteeing an outcome.
AI assistants — whether pulling from training data or a live web search step — do better with brands that are easy to categorize. If your website, your reviews, and your third-party mentions all consistently describe you the same way ("pickleball club in Austin," not sometimes "sports facility" and sometimes "racquet sports venue"), a model has an easier time confidently associating you with the exact question a buyer asked. Category ambiguity doesn't just confuse search engines — it gives a language model less to work with when it's deciding who to name.
This is the single most consistently cited factor across the research available. Ahrefs' analysis of AI visibility across roughly 75,000 brands found that mentions on YouTube had the strongest correlation with AI visibility of any signal tested, and branded web mentions overall (press, review sites, comparison content) also correlated strongly — see Ahrefs' brand visibility correlation study. Separately, reporting on how ChatGPT surfaces recommendations points to third-party validation — press coverage, analyst mentions, and consensus across high-authority sources like Wikipedia — as a bigger factor than on-page SEO tricks (Profound). Practically: get covered by real publications in your space, get listed in real comparison/roundup articles, and don't manufacture fake ones — a model summarizing thin or spammy sources tends to reflect that thinness right back.
Review consensus feeds AI answers the same way it feeds local search rankings. If your average rating is mediocre or your reviews are inconsistent across platforms, that's a real, visible signal a model can pick up on when it's assembling a recommendation — not a rumor. This isn't something you can shortcut convincingly; it's downstream of actually being good at what you do and asking real customers to say so.
Write for the actual question, not the keyword. "What's the best X for Y" is close to how a real buyer phrases it to an AI assistant — content that answers that framing directly, in plain language, with a clear point of view, is easier for a model to extract and quote than content built around keyword density. Structure helps too: clear headers, direct answers near the top, and content a model can lift a sentence from without needing to infer meaning across paragraphs.
Wrong or outdated information about your business anywhere prominent online — an old Google Business Profile listing, a stale "About" page, conflicting details across directories — can get folded into what a model believes about you, and there's no way to "correct" a model after the fact except by fixing the source. Accuracy is unglamorous, but it's one of the few genuinely controllable levers here.
The only reliable way to know is to ask the models the same questions your buyers ask and read what comes back — not to infer it from search rank or guess from vibes. That's exactly what Mentioned does: it generates 5 realistic buyer questions for your category, asks Claude each one independently, and shows you the real snippet where you're mentioned, or names who got recommended instead when you're not. Run it before and after you make changes, and treat any single check as one data point, not a verdict — model answers vary, so the trend over repeated checks matters more than any one run.