Track and Win Gemini With Answer Radar

What the engine records, and how it differs from Google AI Overviews

For each approved question, Answer Radar asks Gemini once with grounding enabled and no history. It stores the full answer, whether Gemini grounded it in a search, the sources it attached, each with its domain and a flag for you, a tracked competitor or a third party, and every brand named with whether it was recommended or only listed.

Gemini decides when to ground, and grounding does not guarantee a citation. Google's own help page says sources appear 'when sources are available'. A poster in r/GeminiAI, in the thread 'Gemini should be the best AI on earth. Google has every advantage possible. So why does it keep feeling like a beta product?', put it from the outside: 'The sourcing is inconsistent'.

Gemini is not Google AI Overviews. The assistant answers a chat question; the Overview appears on a Google results page for some searches and not others. Answer Radar measures them as two engines, and their numbers are never pooled.

Running the first audit that includes Gemini

Nothing extra to enable. Approve the buying questions, press Run audit, check the estimate such as '12 approved questions across 5 answer engines', and press Confirm & run.

Write category questions in the words a buyer uses. A poster in r/GoogleGeminiAI titled a thread 'How can I monitor Gemini sources effectively? I'm tracking AI citations for my brand's SEO', which is exactly the job; the answer is a frozen question set re-run on a schedule, not a one-off check.

Reading the Gemini rows

Open a run from History. Under the heading Gemini, every question has a row with a Searched pill when Gemini grounded the answer and one of three chips: You: recommended, You: mentioned or You: not mentioned. Expand it to see Recommended followed by the brands put forward, or No brand recommended, then the answer and a Citations table with the columns Domain and Who.

The row to learn is the one with a Searched pill and the line No citations recorded. Gemini looked and did not attach a source. That answer is graded thin, is kept out of presence and recommendation rates, and is reported with its reason. It is not a provider failure, which would read as an error, and it is not zero visibility.

In the Citations table the Domain column names the publisher even when Gemini returned a redirect link, because Answer Radar follows the redirect to the real host. In measured customer answers the most cited domains were salesforce.com, 11x.ai, aloware.com, apple.com and lorikeetcx.ai.

From a thin or competitor row to a fix

Start with grounded rows where a competitor sits under Recommended. Open the gap, read the answer, and look at the cited publishers. If a competitor's own page is cited, the content gap view shows which requirement your page fails; if a review or comparison page is cited, the fix is a mention there. A commenter in r/GEO_optimization, in the thread 'Spent a day running the same brand queries through ChatGPT/Claude/Perplexity/Gemini', put the rule: 'Different models clearly trust different authority signals.'

Thin rows need patience rather than a fix. Gemini attached no source, so there is no cited page to change. Watch the question across runs; when Gemini cites for it, the next run shows who.

Drafting is your assistant's job, not ours. Pull the gap and its evidence through the connector, draft the page or the outreach, save it as a fix, then re-run the audit and read the observed change.

Where the numbers appear

In the app, the project overview and each run page show presence and recommendation for Gemini over grounded answers, with the thin count beside them. Through the connector, answer_metrics_get and answer_trends_get return the Gemini segment with its disclosure block, which names the sample counts and grades behind every number. The free check on the Gemini tracker page runs one question against this engine.

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