How to track your brand mentions across AI assistants

To track your brand across AI assistants, ask the same buyer questions to ChatGPT, Perplexity and Google AI Overviews on a schedule, record whether each answer names you or a competitor, and log your position and sentiment over time. Because answers shift run to run, a single check is noise; a cadence turns it into a trend you can act on.

When buyers ask AI assistants questions about your category, those assistants either cite your brand and pages, or they cite competitors instead. The challenge is that you cannot see this happen in real time. AI answers are generated on the fly, invisibly, across thousands of daily queries. jujuGEO solves this by running continuous probes of your category's most important buyer questions across ChatGPT, Perplexity and Google AI Overviews, then showing you exactly where you're cited, where you're missing, and what sources are cited instead.

How jujuGEO tracks your brand mentions

First, jujuGEO identifies the buyer questions that matter in your category, the ones people actually ask AI engines when evaluating solutions. For each question, it probes the live engines and logs whether they mention you, what position you appear in, your share of voice versus competitors, and which source domains they cite. This gives you the complete picture: not just whether you're mentioned, but also which specific competitors and content sources the engines trust.

Second, for every gap, a question where an engine cites a rival instead of you, jujuGEO drafts the actual answer-ready fix: a complete, citable answer, an FAQ, and valid schema, all in the question's language. You review and publish this to your own site. Then jujuGEO re-probes the engines to measure whether the citation actually moved.

This approach works because AI answer engines cite specific sources based on content relevance, authority, and schema signals. By fixing content gaps on your own domain, you earn citations naturally, not by manipulating the engines, but by becoming a legitimate source the engines recognize and trust.

What to record on every run

• Mention / citation: are you named or cited in that engine's answer?
• Position: lead recommendation, mid-list, or footnote?
• Share of voice: your presence versus the competitors named in the same answer.
• Cited-instead domains: which sources that engine used when it did not cite you.
• Sentiment: the tone of the mention when you do appear.

Put side by side, these turn "do I show up in AI?" into a precise map: cited in Perplexity, absent in ChatGPT, footnote in Google AI Overviews. Each cell is a specific gap, not a vague visibility score. See a sample citation-tracking report.

Cross-engine mention timeline (illustrative)

Answers shift by run and by engine. A single check is noise; a cadence shows the trend.

Why social listening and GA are not enough

Social media mentions track discussion of your brand on public platforms. AI mentions track whether your brand is recommended by AI assistants when users ask buying or research questions. AI assistants generate answers behind closed walls, so the responses do not appear in Google search results or social feeds. Traditional analytics cannot see them. You need direct probes of the live engines.

Track on a cadence, then act on the gaps

AI recommendation patterns change when you publish new content, competitors update pages, or engines refresh retrieval. Continuous scheduled probes catch drops faster than a quarterly manual sweep. When you are absent and competitors are named, read the cited-instead domains and the answer language, publish the strongest fix, then re-run the same prompts so you can see whether citation rate or share of voice moved.