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You can use Gemini with Google Search grounding to observe what it answers for a fixed set of brand-related prompts, and BigQuery to analyze eligible data over time. But there is an important constraint: Google’s Gemini API terms restrict caching and analyzing Grounded Results, so do not build a persistent response archive or visibility score unless the live terms expressly allow your intended use. The result is a measurement of a defined sample—not a universal ranking of your brand in AI search.
What this analyzer measures
A useful analyzer has two separate data streams, each answering a different question. Grounded Gemini observations show what the model returned for your selected prompts and the searches it chose to execute. Search Console data shows conventional Google Search performance for your site. Neither stream substitutes for the other.
| Data stream | What it can show | What it cannot establish |
|---|---|---|
| Gemini Search grounding observations | The answer Gemini gave to a particular prompt and available citation metadata for the sources used. | A stable, universal ranking of AI visibility or the frequency with which all AI systems mention your brand. |
| Search Console performance export | First-party Search Console performance data for your site, exported daily to BigQuery. | Whether Gemini or another AI answer cited your brand. |
Keep the unit of measurement explicit. A brand mention, a citation to your site, a citation to a third-party source about your brand, an answer position, sentiment, and share of sampled answers are different measures. Report them separately rather than combining them into an unexplained score.
Check the terms before saving or analyzing grounded answers
Gemini grounding can return useful attribution, but that does not automatically make the results available for indefinite storage or downstream analysis. Google’s Gemini API Additional Terms of Service say that prompts, contextual information, and output used for Search Grounding are stored for 30 days for creating Grounded Results and Search Suggestions. The terms also restrict caching, syndicating, reselling, analyzing, training on, or otherwise learning from Grounded Results and Search Suggestions, subject to stated exceptions.
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That restriction matters to the proposed BigQuery workflow: retaining answer text and citation details to calculate a long-term brand score is analysis of grounded results. Do not assume that the API’s ability to return data authorizes that use. Review the live terms and applicable exceptions for your specific use before persisting or analyzing responses. If your intended workflow is not permitted, do not put grounded answer content or citation metadata into a longitudinal analytics table. Design the collection to render results as required by the terms, and retain only information whose storage and use are allowed.
The schema and SQL patterns below are implementation recommendations, not Google-prescribed requirements. They apply only to data you are permitted to retain and analyze. If you cannot establish that permission for grounded results, use the patterns for a separately authorized data source or limit your implementation to an allowed, transient display.
Understand what grounding returns
With Google Search grounding enabled, Gemini can decide whether search will improve its answer, run one or more searches, and synthesize the results. The response can include answer text, citation annotations, grounding chunks, and mappings between answer segments and source chunks. The Gemini grounding documentation describes this structured attribution. It is more informative than plain answer text, but it does not make an answer a comprehensive or independently verified survey of the web.
When you are allowed to handle the result, preserve the associations between answer segments and cited sources rather than saving only a flattened list of URLs. Keep any response links and suggestions attached to their response, and follow Google’s display and use requirements. Search grounding is not a general-purpose crawling, indexing, or link-collection service.
Design a repeatable prompt sample
Start with a documented set of questions that a potential customer might ask about your category, product, or alternatives. Use the same wording and collection conditions for each scheduled run if you want to compare observations over time. Exploratory prompts can be valuable for discovery, but mark them separately: changing the prompt set changes the sample.
Document the sampling rules
- Save the exact prompt text and a stable prompt identifier.
- Record the selected model identifier and collection timestamp for every run.
- Record locale, geography, or other request settings when applicable; do not compare samples as if they shared settings when they did not.
- Record the prompt-set version, collection configuration, and whether the run was scheduled or exploratory.
- Define each metric before collection—for example, whether a “mention” means an exact brand name in answer text, and whether a “citation” means a source URL attributed to an answer segment.
- For every reported result, show the sample size, date range, model, prompt-set version, and controlled locale or geography.
These are design choices for reproducibility, not a Google standard. A fixed sample improves comparability, but it does not make the sample representative of every question users ask or every answer Gemini could return.
Use a governed data model
First determine which fields you may retain under the terms that apply. If storage and analysis are permitted, a practical normalized design separates run metadata, prompts, answers, and citation relationships. This avoids losing which prompt produced which answer or which source was associated with which answer segment.
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|---|---|---|
prompt_runs |
run_id, prompt_id, prompt_version, model_id, run_timestamp, locale/geography settings, collection configuration |
Defines the sample and the conditions under which an observation was made. |
grounded_answers |
run_id, permitted answer text or answer reference, response status, permitted response metadata |
Connects the response to its run; include content only if the terms allow it. |
grounding_sources |
run_id, permitted source reference, citation annotation or segment association |
Preserves attribution structure if retaining and analyzing that metadata is allowed. |
search_console_daily |
Fields from the Search Console export, with the export date and site/property context | Keeps conventional organic-search performance in its own data stream. |
Apply least-privilege access, documented retention, and deletion procedures to any data you are permitted to store. Keep Search Console data distinct from grounded-response data; a shared date or brand dimension does not make their metrics equivalent.
Bring Search Console data into BigQuery
Google documents daily export of Search Console performance data to BigQuery for more complex analysis through its BigQuery integration documentation. Treat that export as a first-party search channel. Impressions and clicks are Search Console performance measures; they are not counts of AI answer appearances or citations.
A useful comparison is often by date range or campaign context, not a row-level join between an AI response and an organic-search click. For example, you can chart an allowed sampled-answer measure beside Search Console clicks over matching dates, while labeling them as separate measures and avoiding a causal claim. Ensure the time zone, date boundary, property, and aggregation level are aligned before comparing trends.
Analyze only data you are allowed to use
If your terms review establishes that retaining and analyzing a grounded result is permitted, store the observation before aggregating so the metric can be audited. The following example assumes a permitted normalized table called grounded_observations with one row per prompt run and a boolean brand_mentioned field. It calculates the share of sampled runs with a mention; it is not a score validated by Google.
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DATE(run_timestamp) AS run_date,
model_id,
prompt_version,
locale,
COUNT(*) AS sampled_runs,
COUNTIF(brand_mentioned) AS runs_with_mention,
SAFE_DIVIDE(COUNTIF(brand_mentioned), COUNT(*)) AS mention_share
FROM `project.dataset.grounded_observations`
WHERE DATE(run_timestamp) BETWEEN @start_date AND @end_date
GROUP BY run_date, model_id, prompt_version, locale
ORDER BY run_date, model_id, prompt_version, locale;Use a consistent denominator. If some prompts fail, omit them from the successful-response denominator but report their failure count separately; otherwise a change in API or collection reliability can look like a change in visibility. Likewise, a source-citation metric should define whether it counts responses citing your own site, distinct source URLs, or answer segments with a citation. Keep those counts separate from mentions.
Schedule recurring BigQuery analysis
BigQuery scheduled queries can run recurring GoogleSQL and support parameters such as run date and time. Google documents them as a BigQuery Data Transfer Service feature, with IAM requirements, job quotas, and pricing like manual queries. See scheduled queries documentation for current setup details.
- Confirm that the underlying data may be retained and analyzed, and that the account running the job has the required BigQuery and transfer permissions.
- Validate the query manually against a known date range and check the output, including nulls, duplicate runs, and the denominator.
- Create the scheduled query with a documented cadence and output destination. Use a schedule away from exactly the top of the hour for insertion workflows unless the query is idempotent; Google cautions that schedules at the hour can trigger more than once.
- Make reruns safe. For example, write results by date using a replace-or-merge approach rather than blindly appending the same date on every retry.
- Review query completion, errors, cost, and output volume after the first scheduled executions.
Recurring SQL automates a calculation; it does not validate the prompt sample, metric definition, citations, or any claim that the output represents general AI visibility.
Independent reader supportYour contribution helps us test, update, and keep practical guides available for everyone.Monitor runs without mistaking health for accuracy
Google documents alerts on scheduled-query row-count metrics through Cloud Monitoring scheduled-query alerts. A row-count change can flag an empty or unexpectedly large result, which is useful for operational monitoring. It cannot show that the returned citations are accurate or that your metric is meaningful. Also monitor successful completion and query errors, and investigate changes in prompt coverage or failed calls.
Control grounding costs and optional AI assistance
Grounding cost depends on model behavior and current pricing. Google’s grounding documentation says Gemini 3 billing counts each search query the model decides to execute, while Gemini 2.5 and older are described as billed per prompt. A single request may therefore result in multiple searches for Gemini 3. Confirm current model support and pricing before estimating a production budget; model names and pricing can change.
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Gemini in BigQuery is optional, not a requirement for this analyzer. It can assist with explaining or generating SQL and analyzing datasets, but setup requires enabling APIs and granting roles. Google’s Gemini in BigQuery setup documentation warns that the feature does not support all BigQuery compliance and security offerings. Check that limitation against your project requirements before enabling it; ordinary GoogleSQL can run without Gemini in BigQuery.
What conclusions are safe to report?
Describe findings as observations from the tested sample: for example, “In this week’s 40 fixed prompts, the selected model mentioned the brand in 12 successful answers.” Include the run period, model, prompt-set version, locale, and sample size. If reporting source citations, identify the precise counting rule and distinguish citations to your own site from citations to other sources.
Do not generalize that result to all AI search, all users, or an overall brand ranking. Gemini’s grounding process can choose whether to search and what sources to use; prompt wording, model, locale, and timing affect what you observe. Search Console clicks and impressions can add useful context about conventional search performance, but they do not validate or explain AI answer visibility by themselves.
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