From Anker SOLIX to xTool — the brands above already see how ChatGPT, Gemini and Perplexity mention, cite and recommend them. Your brand is being talked about in AI right now. See it.
How to Track Brand Mentions in Gemini: A 2026 Guide | GEOly | GEO Data Platform for DTC Brands
How to Track Brand Mentions in Gemini: A 2026 Guide
Summary
Tracking brand mentions in Gemini takes a 30–50 prompt set built from real buyer questions, 3–5 sampled runs per prompt, separate mention and citation logs, and an engine-filtered tracking tool — the Gemini app passed 750 million monthly users in Q4 2025, too big a channel to eyeball.
2026/07/05
8 min read
To track brand mentions in Gemini, you need four things: a prompt set built from real buyer questions, a fixed sampling routine inside the Gemini app, a log that separates mentions from citations, and a tracking tool with a Gemini engine filter to automate the loop once manual checks stop scaling. The Gemini app passed 750 million monthly active users in Alphabet's Q4 2025 earnings, which makes it one of the largest AI answer surfaces anywhere — and one of the least tracked, because most teams conflate it with Google AI Overviews. They are different surfaces with different citation behaviors, and this guide covers both the distinction and the workflow.
Key takeaways
Gemini (the standalone app and gemini.google.com) is a separate surface from Google AI Overviews and AI Mode; at I/O 2026 Google merged the latter two into one AI Search experience while the Gemini app stayed distinct.
Gemini only attaches a Sources button when it decides to link out, so a large share of brand mentions in Gemini carry no citation at all — mention rate and citation rate must be logged as separate metrics.
Gemini's grounding literally runs Google Search queries behind the scenes, per the Gemini API documentation, so pages that are indexed and rank organically have a structural head start on getting cited.
Answers are non-deterministic: sample each prompt 3–5 times before calling any result a trend.
A 30–50 prompt set on a weekly cadence is the minimum viable tracking program; daily automated runs are the realistic end state.
Get the map right: Gemini vs AI Overviews vs AI Mode
"Gemini" names two things at once: the model family powering nearly every Google AI surface, and the standalone assistant at gemini.google.com and in the Gemini mobile app. When marketers ask how to track brand mentions in Gemini, they almost always mean the assistant — the chat product that answers buying questions the way ChatGPT does.
AI Overviews and AI Mode live somewhere else entirely: inside Google Search. AI Overviews are the generated summaries above classic results; AI Mode is the conversational search tab. At I/O 2026, Google merged the two into one seamless AI Search experience running Gemini 3.5 Flash by default, and reported 2.5 billion monthly users for AI Overviews and over 1 billion for AI Mode.
The distinction matters for tracking because retrieval differs. AI Search surfaces are search-first: they retrieve, then summarize, and citation slots are baked into the layout. The Gemini app is model-first: it answers from model knowledge and only grounds through Google Search when it judges that a search will improve the answer. A brand can be well-cited in AI Overviews and nearly invisible in the Gemini app on the same query. Track them as separate engines, and never average them together.
Step 1: Build a prompt set from real buyer questions
Skip generic keywords. Pull the questions people actually ask before buying: sales call transcripts, support tickets, on-site search logs, Reddit threads, People Also Ask boxes. Then sort them into four buckets:
Category prompts: "best cordless stick vacuum under $300"
Comparison prompts: "[your brand] vs [competitor], which is better"
Problem prompts: "vacuum that handles pet hair on thick carpet"
Thirty to fifty prompts is the sweet spot — enough to cover the funnel, small enough to maintain. One wrinkle specific to Gemini and AI Mode: fan-out. A single question gets decomposed into several hidden sub-queries, and your brand can enter or exit the answer through a sub-query you never wrote down, so include phrasing variants of your highest-stakes questions. For the full metric framework behind all this — mention rate, citation rate, Share of Model — see the companion guide to AI search visibility KPIs.
Query fan-out tracking: how ChatGPT expands buyer questions into web search queries, with popular searches and demand themes — Source: GEOly AI (app.geoly.ai)
Step 2: Run and sample your prompts in Gemini
Open gemini.google.com in a clean browser profile so your own history doesn't contaminate results. Record the model version (the default Flash model answers differently than Pro), the date, and your country — all three shift answers. Then run each prompt three to five times. Gemini is non-deterministic; one run is an anecdote, five runs is a sample.
While you run, watch for the Sources button. Per Google's help documentation, sources appear as a button at the bottom of a response or inline within it, opening a side panel of links — and when the button is absent, Gemini attached no links to that answer at all. That absence is data. Log it.
Step 3: Log mentions, citations, and sentiment
A minimal spreadsheet needs these columns: prompt, date, model, brand mentioned (yes/no), position (recommended first vs named among five), cited (yes/no, plus which domains), sentiment, and competitors named.
Keep mentions and citations strictly separate. Gemini regularly names brands without linking anywhere, and the two numbers move independently. Three Gemini-specific citation behaviors are worth knowing:
No links is normal. Google's docs are explicit that not every response includes sources; a missing Sources button means no links were provided for that answer.
Direct quotes always link. If Gemini quotes a large amount of text from a webpage, that page appears in the sources list.
Grounding is Google Search. The Gemini API's grounding documentation shows the model generating and executing real Google Search queries, then mapping citations to specific text spans. Classic SEO fundamentals — indexation, rankings, crawlability — feed Gemini citations directly.
Sentiment deserves its own column. Gemini often qualifies its recommendations ("a solid budget pick, though the app experience trails competitors"), and a caveated mention converts differently from an outright endorsement. Log the qualifier, not just the mention.
Step 4: Automate the loop with a tracking tool
Manual sampling holds up for two or three weeks. Then someone skips a Friday, the sample sizes drift, and the trend line stops being trustworthy. This is the point to automate. Disclosure: GEOly is our product.
A worked example with GEOly (free to start): add your brand and competitors at app.geoly.ai, import or auto-generate your prompt set, and the platform re-runs every prompt daily across ChatGPT, Gemini, Perplexity, Grok and Google AI Overviews. Open the Engines view and filter to Gemini: mention rate, citation rate and Share of Model become trend lines instead of snapshots, with fan-out queries captured automatically. Because the same prompts run on every engine, the Gemini column doubles as a cross-engine comparison at no extra effort.
Cross-platform visibility matrix comparing brand mentions across ChatGPT, Gemini, Google AI Overview, AI Mode and Perplexity — Source: GEOly AI (app.geoly.ai)
If your team lives in Claude Code or Cursor, the same data is reachable through GEOly's MCP server — handy for piping Gemini visibility numbers into a weekly report without opening a dashboard.
Step 5: Benchmark against competitors
An absolute mention rate means little on its own. Forty percent sounds respectable until you learn a competitor sits at seventy-five on the same prompts. Share of Model — your share of Gemini's answers versus competitors — is what makes the numbers decision-ready.
Two patterns deserve attention. First, prompts where you and a competitor swap places between runs: that is contested territory, and marginal content improvements can tip it. Second, prompts where a competitor gets cited and you are not even named: that is a sourcing gap, not a content-quality problem, and it needs different treatment. And remember visibility rarely transfers between engines — strong ChatGPT presence does not imply Gemini presence, because the retrieval stacks differ. The broader workflow for tracking brand mentions across AI search applies engine by engine.
Step 6: Act on the citation sources Gemini favors
Aggregate the domains Gemini cites across your whole prompt set. Because grounding runs through Google Search, the cited set usually overlaps heavily with what ranks organically for those questions — review sites, comparison posts, Reddit threads, retailer pages. That list is your roadmap: pitch the review sites Gemini keeps citing, refresh your own pages that rank but never get quoted, and add the direct, quotable passages that grounding loves to lift. Jake Ward's framing fits exactly here: the goal is to get cited, not clicked.
Commerce brands have one more layer. Gemini-powered surfaces increasingly return product cards, not just prose, and text-citation tracking misses that shelf entirely. If you sell physical products, extend the same discipline to the AI shopping shelf — the Share of Card metric covers how. For more on the measurement side of GEO, Riven Gao writes regularly on this beat; the AI visibility tag collects the lot.
FAQ
Does Gemini show sources?
Sometimes. When Gemini has links to offer, a Sources button appears at the bottom of the response or inline, opening a side panel — but many responses ship without any sources, which Google confirms in its help docs. Direct quotes from webpages are the exception: those always link back. Treat a citation as a separate, harder-to-earn event than a mention.
How is Gemini different from Google AI Overviews?
Gemini is the standalone assistant (app and gemini.google.com); AI Overviews are the generated summaries inside Google Search, merged with AI Mode into a single AI Search experience at I/O 2026. Both run on Gemini models but retrieve differently: AI Search is retrieval-first with built-in citation slots, while the Gemini app answers from model knowledge and grounds through Google Search only when needed. Track them as separate engines.
Can I track Gemini visibility for free?
Yes, two ways. Manually: a 30–50 prompt set, a spreadsheet, and an hour of weekly sampling cost nothing. Automated: GEOly is free to start and includes Gemini in its engine coverage alongside ChatGPT, Perplexity, Grok and Google AI Overviews, so you get daily runs and trend lines without a paid plan.
How often should I re-check brand mentions in Gemini?
Weekly is the floor for manual tracking; daily is standard once automated. Gemini's answers shift with model updates and grounding decisions, so a single-run snapshot misleads. Sample each prompt several times and read trends over weeks rather than reacting to day-to-day noise.
