How to Track Whether AI Mentions Your Brand (ChatGPT, Perplexity, Gemini)

To track whether AI mentions your brand, pick the real buyer questions your customers ask, run them across ChatGPT, Perplexity, Gemini, and Google AI Overviews, and record for each one whether you appear, how you are described, and which competitor is named. Do it on a fixed schedule so you can spot changes. The manual version works for a monthly snapshot but does not scale, because answers vary by phrasing and change over time. Automated monitoring tools run the prompts across every engine on a schedule, score your visibility, and alert you when you appear, drop out, or lose ground, so you react to changes instead of discovering them late.

Improving your AI visibility only works if you can see it. Otherwise you are making changes and hoping. This guide covers how to track whether AI engines name your brand, starting with a simple manual routine and moving to automated monitoring, so you always know where you stand and notice the moment it changes.

Tracking is only useful if you track what buyers actually ask. Guessing at keywords misses the point; you want the real decision-moment questions.

Pull them from three places: the questions prospects ask in sales calls, the questions that land in your support inbox, and the natural-language prompts a buyer would type into an AI. Aim for a stable set of six to ten, a mix of shapes:

• "Best [category] for [specific buyer]?"
• "Alternatives to [a known competitor]?"
• "Who should I use for [the job you do]?"
• "Is [your brand] any good?"

Keep this list fixed. Tracking the same prompts every time is what lets you compare one week to the next.

Your brand can be recommended on one engine and absent on another, so check them all, not just ChatGPT.

Cover the surfaces your buyers actually use: ChatGPT, Perplexity, Gemini, and Google AI Overviews at a minimum, plus Claude and Grok if your audience leans that way. Run each of your fixed questions on each engine, because the same question often gets a different answer depending on where it is asked. Perplexity in particular shows its citations, which tells you not just whether you appear but which source it trusted to get there.

A yes-or-no on presence is too thin to act on. Capture the fuller picture for each prompt and engine.

• Presence: were you named at all.
• Accuracy: was the description current and correct, or stale and wrong.
• Position: were you the first option offered or a footnote.
• Competitor share: who got named when you did not.

That last column is the most useful. Knowing exactly which competitor wins the answer for a given question tells you who to study and what gap to close. Presence tells you there is a problem; competitor share tells you where to aim.

A single check is a snapshot of a moving target. AI answers change constantly, as models update, as competitors sharpen their pages, and as new sources get cited, so the spot you owned last month can quietly go to someone else this month.

Pick a cadence and hold it, monthly at least, weekly if the category moves fast. The value is in the trend line: are you appearing more often, being described more accurately, climbing above competitors. A number you only look at once tells you almost nothing; the same number tracked over time tells you whether your work is landing.

The manual routine is a great way to learn what to watch, and it is fine for a monthly snapshot of a handful of prompts. But it breaks down fast. Ten prompts across six engines is sixty checks, every cycle, and you still have to log the results, spot the changes, and remember to do it at all. Most people quit after a few weeks.

That is the point to automate. A monitoring tool runs the whole grid for you on a schedule and surfaces only what changed.

Cituna is built for this exact job. It runs your buyer questions across ChatGPT, Perplexity, Gemini, Claude, Grok, and Google AI Overviews on a schedule, scores your AI visibility, and shows you where you appear, where you dropped out, and which competitor took the spot, without you re-typing a single prompt.

Two things make the monitoring actionable rather than just interesting. It generates the specific fixes (schema, FAQ, and llms.txt) for the gaps it finds, so tracking leads straight to doing. And it connects to Google Search Console, so you can tie the AI answers back to real clicks and impressions, and prove that improving the answer improved your traffic, not just your ego. Set it up once, and you trade sixty manual checks a cycle for a dashboard that tells you what moved and what to do about it.

Write the five questions a customer would actually ask — the problem, not your brand name — and put them to ChatGPT, Perplexity and Gemini today. A spreadsheet of who got named is your baseline; tracking only means anything against it.