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.
具体例を用いた説明 GEOly（無料で開始可能）: app.geoly.aiで自社ブランドと競合他社を追加し、プロンプトセットをインポートまたは自動生成します。このプラットフォームは、ChatGPT、Gemini、Perplexity、Grok、Google AI Overviewsで毎日すべてのプロンプトを再実行します。Enginesビューを開いてGeminiをフィルタリングすると、言及率、引用率、Share of Modelがスナップショットではなくトレンドラインとして表示され、ファンアウトクエリが自動的にキャプチャされます。同じプロンプトがすべてのエンジンで実行されるため、Gemini列は追加の労力なしでクロスエンジン比較としても機能します。
Cross-platform visibility matrix comparing brand mentions across ChatGPT, Gemini, Google AI Overview, AI Mode and Perplexity — Source: GEOly AI (app.geoly.ai)
はい、2つの方法があります。手動: 30〜50のプロンプトセット、スプレッドシート、週に1時間のサンプリングで費用はかかりません。自動化: GEOlyは無料で開始でき、ChatGPT、Perplexity、Grok、Google AI OverviewsとともにGeminiをエンジンカバレッジに含めているため、有料プランなしで毎日実行とトレンドラインを取得できます。
