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

For fifteen years, “brand monitoring” meant tracking mentions across news, social, and the web. In 2026 there’s a new surface that matters as much as any of them: what AI assistants say about your brand. When a prospect asks ChatGPT “is [your company] any good?” or “what are the best tools for X,” the answer shapes their decision — and most brands have zero visibility into it. Here’s how to actually track your brand mentions in AI, what to measure, and what to do with it.

Why AI brand monitoring is now essential

Traditional monitoring catches what people publish about you. AI brand monitoring catches what machines say about you — often to a buyer at the exact moment of consideration. Three reasons it’s no longer optional:

Buyers ask AI directly. “Best [category] tool,” “[competitor] vs [you],” “is [you] legit” — these questions now go to ChatGPT and Perplexity as often as to Google.
AI answers are confident and consequential. A model stating “the leading options are A, B, and C” — with you absent, or worse, described inaccurately — quietly costs you deals you never see.
You can’t fix what you can’t see. Unlike a bad review you can respond to, an AI misrepresentation is invisible unless you’re monitoring for it.

What to actually measure

“Track brand mentions in AI” breaks into four concrete metrics:

Mention rate: across a set of buyer-intent questions asked without naming you, how often does the answer mention your brand? This is your headline number.
Share of voice: when you’re mentioned (or not), who else is? Tracking competitor mentions alongside yours shows your relative standing and, crucially, who’s winning the answers you’re losing.
Sentiment & accuracy: how are you described? Positive, neutral, negative — and is it correct? AI models confidently state outdated pricing, wrong features, and occasional hallucinations. Catching these is half the value.
The receipts: the actual answer text, saved. “We’re named in 6 of 20 answers, usually positively, but ChatGPT says we don’t have a free tier (we do)” is an actionable finding. A bare number isn’t.

How to set up AI brand monitoring

Step 1 — Build your question set

This is the foundation. Write 20–40 real questions your buyers ask, in four buckets:

Category questions: “best [category] tools,” “top [category] software 2026.”
Comparison questions: “[you] vs [competitor],” “[competitor] alternatives.”
Brand questions: “is [you] good,” “[you] pricing,” “[you] reviews.”
Problem questions: the pain points your product solves (“how do I [job your product does]”).

These questions are your monitoring program — everything else measures against them.

Step 2 — Query the engines the way buyers do

Ask each question to ChatGPT (with browsing), Perplexity, and Gemini — the three that matter most, plus Copilot and Google AI Overviews if relevant. Ask them cleanly (don’t lead with your brand name — you want unprompted mentions). Record for each: were you named, who else was, the sentiment, and the full answer text.

Step 3 — Do it on a cadence, not once

A single snapshot is a data point; monitoring is a trend line. AI answers shift as models update and competitors publish. Re-run weekly or monthly and watch the direction. Manually, that’s a few hours per cycle across dozens of prompts and three engines — which is exactly why dedicated AI brand monitoring tools exist.

Manual vs. tooling

You can start manually today — a spreadsheet, your question list, and 30 minutes in the three engines. It’s tedious but real, and it beats flying blind. The limits show up fast: it doesn’t scale across many prompts, it’s hard to keep consistent week-over-week, and capturing/organizing the receipts by hand is painful.

AI brand monitoring tools (Serpair among them) automate the loop: run your prompts across the engines on a schedule, score your unprompted mention rate, track the competitor leaderboard, capture the answer text automatically, and trend it over time. The right one also connects it to your SEO data — because your AI mentions and your search rankings are two views of the same visibility problem. But whether you automate or not, the discipline is identical: define the questions, query the engines unprompted, capture the receipts, and track the trend.

Designing questions that actually reveal your standing

The quality of your monitoring is capped by the quality of your questions, so it’s worth doing well. A few principles I use:

Ask questions, not keywords. “Best rank tracking software for agencies” (a question a human asks an AI) reveals far more than the keyword “rank tracker.” Phrase them the way a real buyer types into ChatGPT.
Never lead with your brand. The whole point is unprompted mentions. Asking “why is [you] the best X” guarantees you’re named and measures nothing. Ask the neutral question and see if you surface on your own.
Cover the full funnel. Top-of-funnel category questions (“what tools do X”), mid-funnel comparisons (“[you] vs [competitor]”), and bottom-funnel brand checks (“is [you] worth it”). Each reveals a different part of your AI presence.
Include your competitors’ brand terms. Tracking “[competitor] alternatives” tells you whether you show up when their customers go looking — often the highest-intent moment there is.
Refresh the set quarterly. As your market and products evolve, so should the questions. A stale question set measures a business you no longer are.

How this differs from traditional brand monitoring

If you already run social/press monitoring, don’t assume it covers AI. They’re fundamentally different:

	Traditional monitoring	AI brand monitoring
Source	What people publish (news, social, reviews)	What models say when asked
Trigger	A person posts about you	A buyer asks an assistant a question
Visibility	Public and findable	Invisible unless you query for it
Fix	Respond, engage, PR	Improve the source pages + entity signals the model reads
Metric	Volume, reach, sentiment	Mention rate, share of voice, accuracy

The critical gap: traditional tools will never surface an AI misrepresentation, because nothing was “published” — the model generated it live, to one buyer. Only querying the engines directly reveals it. That’s why AI brand monitoring is a distinct practice, not a feature of your existing social listening tool.

What to do with what you find

Monitoring without action is just anxiety. Map findings to moves:

Low mention rate? You have a GEO problem — work the AI visibility playbook: machine-answerable content, schema, llms.txt, entity recognition, and corroborating mentions.
A competitor dominates a question? Read the pages the model cites for that question and out-execute them (more specific, better-structured, better-evidenced content).
Inaccurate claims about you? Find and fix the source pages the models are reading — clarify your pricing/features on your own site and in the third-party references they rely on.
Negative sentiment? Investigate what’s driving it (often an old review or outdated comparison ranking highly) and address the source.

A realistic expectation

Two honest notes. First, you won’t influence AI answers overnight — retrieval indexes lag, so improvements to your mention rate show up over weeks. Second, you won’t control the narrative completely — these are probabilistic systems. The goal isn’t perfection; it’s a rising mention rate, accurate representation, and a closing gap on competitors, tracked like any other KPI.

Bottom line

Your buyers are asking AI about you right now, and the answers are shaping decisions you never see. Tracking your brand mentions in AI — mention rate, share of voice, sentiment, and the receipts — turns that invisible surface into a measurable one. Start with a spreadsheet and 20 questions if you must; graduate to a tool that runs it continuously and ties it to your search data. Either way, monitor it like you monitor rankings, because in 2026 it’s just as much a part of whether people find, trust, and choose you.

How to improve your AI visibility — once you are tracking mentions, how to raise them.
Generative Engine Optimization: the 2026 playbook — the discipline that drives AI brand mentions.
Profound alternatives: AI visibility tools compared — other tools for monitoring AI brand mentions.