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How to Improve Your AI Visibility: Getting Named in ChatGPT, Perplexity & Gemini

A step-by-step guide to improving your brand's AI visibility — how to get recommended by ChatGPT, Perplexity, and Gemini, how to measure your mention rate, and how to close the gap on competitors.

The Serpair Team · SEO & GEO Strategy · ·9 min read

“How do we show up when someone asks ChatGPT for a tool like ours?” is now the single most common question I get from founders and heads of marketing. It used to be “how do we rank on Google.” The shift is real, and the honest answer is: AI visibility is earnable, but only if you treat it as a measurable program rather than a vibe. Here’s how I’d approach it.

First, understand what “AI visibility” means

AI visibility is how often, and how favorably, AI answer engines name your brand when people ask questions in your category — without being prompted with your name. If someone asks Perplexity “what are the best AI search visibility tools?” and you’re in the answer, you have visibility. If a competitor is named and you aren’t, they have it and you don’t.

It’s measurable in three numbers:

If you’re not tracking these, you’re guessing. There are now dedicated AI visibility tools (Serpair among them) that automate this loop — but you can start with a spreadsheet and 20 questions today.

Step 1 — Baseline your mention rate

Before changing anything, measure. Write down the 15–25 questions your actual buyers ask — not keywords, questions: “best rank tracking software for agencies,” “how do I track brand mentions in AI,” “Profound alternatives.” Ask each to ChatGPT (with browsing), Perplexity, and Gemini. Record: were you named? Who else was? Copy the answer text.

That baseline is your scoreboard. Everything after this is judged against it.

Step 2 — Fix the retrieval foundation

Answer engines can only cite what they can retrieve. Two things break this silently:

Step 3 — Become a recognized entity

Models cite brands they “know.” You build that recognition by being consistent and being referenced:

Step 4 — Publish answer-first content for the real questions

You can’t be cited for a question you haven’t answered. Build a content layer that covers your category comprehensively — what it is, how to choose, how you compare — with each section leading with a direct, quotable answer and using question-style headings. This is the content that gets lifted into AI answers. (It’s also what earns featured snippets and rankings, so it compounds.)

Concretely, the highest-leverage pieces:

Step 5 — Close the competitor gap deliberately

Your share-of-voice leaderboard tells you exactly who to study. When a competitor keeps getting named and you don’t, go read why: crawl their cited pages, look at their structure, their schema, the specificity of their claims. In audits I run, the consistently-cited pages share a pattern — clear answers up top, structured data, concrete numbers, and comprehensive coverage of the sub-questions. Match that bar, then beat it with better evidence.

Step 6 — Re-measure on a cadence

AI answers drift — models update, competitors publish, indexes refresh. Re-run your question set weekly or monthly and watch the trend, not a single snapshot. The goal is a mention-rate line that climbs and a share-of-voice gap that closes. Treat it exactly like a rank-tracking chart, because that’s what it is for the AI era.

Engine-specific tactics that make a difference

The engines aren’t interchangeable, and small adjustments pay off:

A realistic before-and-after

Here’s the shape of a typical engagement, so you know what “good” looks like. A B2B SaaS client came in at a baseline mention rate of about 10% — named in roughly 2 of 20 buyer questions, always behind two better-known competitors. We didn’t do anything exotic: shipped llms.txt and Organization + FAQPage schema, rewrote their top five pages to lead with direct answers and question-style headings, published two comprehensive category guides answering the exact questions buyers asked the engines, and pursued a couple of “best [category] tools” listings for corroboration.

The needle didn’t move in week one — retrieval indexes lag. But over roughly eight to ten weeks the mention rate climbed into the 40–50% range, and on their strongest questions they went from absent to consistently named. Nothing magic happened; they simply became a clearer, better-corroborated, more answerable entity than they’d been — and the models responded. That lag-then-compound curve is normal. Expect weeks, not days, and judge the trend.

Step 6 — Re-measure on a cadence (recap)

The discipline is a loop, not a launch: baseline, improve, wait for the indexes to catch up, re-measure, and close the gap deliberately. The teams that treat AI visibility as an ongoing measured program — the same way they’ve always treated rankings — are the ones who become the defaults the models reach for.

What to expect

AI visibility isn’t instant — retrieval indexes and entity recognition take weeks to catch up to your changes. But it’s durable: once you’re an established, well-structured, frequently-corroborated entity in your category, you become a default the models reach for. That compounding is the whole game.

The shortcut

Doing all of this manually — running dozens of prompts across three engines, logging mentions, tracking competitors, re-running weekly — is exactly the tedium Serpair automates: it measures your unprompted mention rate, shows the competitor leaderboard, captures the receipts, and pairs it with your Google rankings so you optimize both surfaces from one place. But whether you use a tool or a spreadsheet, the discipline is the same: baseline, fix retrieval, build entity recognition, publish answer-first content, close the gap, and re-measure. Do that, and you stop wondering whether AI recommends you — you’ll know.

See your own AI & search visibility

Serpair tracks your Google rankings and measures whether ChatGPT, Perplexity, and Gemini cite your brand — then hands you a plain-English action plan.

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