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There is an important design constraint: Google’s Gemini API terms place restrictions on storing and reusing Grounded Results and Search Suggestions. Check the current terms and required display rules before persisting responses or analyzing them in BigQuery. If your intended use is not permitted, do not build the response-storage and analysis parts of this pipeline.
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What this analyzer can—and cannot—measure
Search grounding lets Gemini decide whether Google Search could improve an answer, run one or more searches, and return an answer with citation annotations and structured grounding data. That data can connect answer segments to grounding chunks, so an analyzer can preserve attribution rather than treating the result as plain text. See Google’s Gemini Search grounding documentation.
The measurement is the result for a particular prompt, model, time, and set of searches. It does not establish a stable position across all AI search, all users, or even all prompts about the same subject. Google’s documentation describes the grounding feature; it does not validate a cross-model brand-visibility score.
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| Data stream | What it observes | What it does not establish |
|---|---|---|
| Gemini Search grounding observations | Answers Gemini returned for the prompts you submitted, plus available citation and grounding metadata | A universal ranking, or how every user’s AI answer appears |
| Search Console performance exported to BigQuery | First-party Search Console performance data for your site, exported daily | Whether, or how often, Gemini cited your brand in an answer |
Keep those streams separate in the report. Search Console is useful context about conventional search performance, not a proxy for AI-answer citations. Google documents the daily export in its BigQuery integration documentation.
Check terms and data governance before collecting results
Do this before designing a table that stores grounded answer text. Google’s Gemini API terms state: “Google will store prompts, contextual information that you may provide, and output for thirty (30) days for the purposes of 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 exceptions listed in the terms. Read the current Gemini API terms, including the applicable exceptions and requirements for displaying results, before deciding whether your intended storage and analytics are allowed.
- If your use is permitted: document the applicable basis, follow the required presentation rules, limit access, and set retention and deletion behavior accordingly. The 30-day service retention term is not a blanket authorization for your own long-term storage.
- If the terms do not permit your intended analysis: do not persist or analyze grounded answers or suggestions for this purpose. A metadata-only design is not automatically compliant; confirm what you may retain and use.
- For any stored data: define access controls, retention, deletion, and the purpose of each field before collection. Treat these as project governance decisions, not as a schema mandated by Google.
Define the sample so observations are comparable
Start with a documented set of questions that represent the ways people might ask about the brand, its category, and relevant needs. Keep the wording fixed for a comparison period. If you change the prompt set, model, or collection settings, record the change rather than blending the new results into an apparently continuous series.
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Choose distinct measures
Do not collapse different observations into an unexplained “AI visibility” number. A brand mention, a source citation, the position of a mention in the answer, sentiment, and the share of sampled answers that mention the brand are separate measures. Define each one and its denominator before reporting it.
- Sample: the exact prompt set and number of completed observations.
- Timing: collection timestamp and reporting date range.
- Model and settings: model identifier and relevant locale or geography settings, when controlled.
- Attribution: answer text and available citation annotations, source references, grounding chunks, and segment-to-source mappings, only where retention and use are permitted.
- Collection configuration: the settings and process used to submit each prompt, so a later run can be compared fairly.
This is a proposed monitoring schema, not a Google-prescribed standard. Retaining the exact prompt and configuration makes the sample easier to interpret; it does not make the results representative of every Gemini answer.
Collect grounded answers with attribution intact
Use the Gemini API’s Google Search grounding capability for the prompt sample. The model may choose whether search would improve an answer and can execute multiple searches. Preserve the returned response and its attribution structure together if your terms review permits that use; do not flatten citations into a separate list that loses which answer segment each source supports. The grounding documentation describes citation annotations, grounding chunks, and support mappings.
Keep source references and suggestions associated with their response, and follow Google’s terms about displaying them. Search grounding is not a general-purpose link collection or crawling and indexing pipeline.
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Google’s documentation describes different grounding billing units: for Gemini 3, each search query the model decides to execute counts; for Gemini 2.5 and older, billing is per prompt. A single request can therefore result in multiple searches on Gemini 3. Check current model support and pricing before estimating a production budget, because model availability and pricing can change.
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Use an explicit, governed BigQuery schema
Only create a table for grounded response content if the intended retention and analysis comply with the current terms. The following is a design sketch for a permitted implementation, not a Google standard or a description of an API response schema.
| Proposed field | Purpose |
|---|---|
observation_id |
Stable identifier for one submitted prompt observation |
collected_at |
Timestamp for the run |
prompt_id and prompt_text |
Identify the fixed sample question and retain its exact wording, where permitted |
model_id and collection_config |
Record the model and relevant run settings |
locale or geography |
Record applicable location settings; leave these absent or null if not controlled |
response_text |
Retain the answer only if permitted by the terms and your retention policy |
grounding_metadata |
Preserve the returned citation and segment-to-source attribution structure where permitted |
collection_status |
Distinguish completed observations from errors or incomplete runs |
Use a separate table or view for Search Console metrics. Keep its actual exported schema intact or document any transformations you make. A reporting layer can aggregate each stream by date and then join the aggregates for comparison; matching dates does not imply that conventional search performance caused or predicts an AI citation.
Independent reader supportYour contribution helps us test, update, and keep practical guides available for everyone.Compare observations with BigQuery SQL
Once you have permission to analyze the grounded observations, use explicit definitions and aggregate the sample before comparing it with Search Console. This illustrative GoogleSQL assumes a proposed observation table with the fields shown above, plus a reporting view named sc_daily with date, clicks, and impressions. Those names are examples for your own modeled tables, not guaranteed names in a Search Console export.
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-- Define a mention in a documented, reproducible way before using this measure.
WITH prompt_runs AS (
SELECT
DATE(collected_at) AS run_date,
COUNT(*) AS completed_observations,
COUNTIF(LOWER(response_text) LIKE '%example brand%') AS responses_mentioning_brand
FROM `project_id.dataset_id.visibility_observations`
WHERE collection_status = 'complete'
GROUP BY run_date
),
search_console AS (
SELECT
date AS run_date,
SUM(clicks) AS organic_clicks,
SUM(impressions) AS organic_impressions
FROM `project_id.dataset_id.sc_daily`
GROUP BY run_date
)
SELECT
p.run_date,
p.completed_observations,
p.responses_mentioning_brand,
SAFE_DIVIDE(p.responses_mentioning_brand, p.completed_observations)
AS share_of_sampled_responses_mentioning_brand,
s.organic_clicks,
s.organic_impressions
FROM prompt_runs AS p
LEFT JOIN search_console AS s USING (run_date)
ORDER BY p.run_date;The example’s text match is deliberately simple: it can miss spelling variants, ambiguous references, and context, and may count an irrelevant mention. It is not sentiment analysis, a citation measure, or a validated score. For defensible reporting, document how each measure is derived and inspect whether the method suits the prompts and language being sampled. Report sample size and date range beside any share; a percentage from a small or changed prompt sample can be misleading.
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Schedule the analysis and monitor its operation
BigQuery scheduled queries can run recurring GoogleSQL and use parameters such as run date or time. They use BigQuery Data Transfer Service features, require appropriate IAM permissions, are subject to BigQuery job quotas, and are priced like manual queries. See Google’s scheduled-query documentation.
- Make the query repeatable: decide whether each run appends a new dated observation, replaces a reporting partition, or updates a defined date range. Choose a write pattern that makes retries safe.
- Set the schedule away from the top of the hour for insert workflows: Google cautions that a schedule exactly on the hour might trigger multiple times. If that timing is necessary, make the query idempotent so a repeated run does not silently duplicate records.
- Grant only the required IAM access: configure the permissions needed for the scheduled query and its destination dataset, then verify that the schedule can run successfully.
- Alert on unexpected row counts and run failures: Google documents Cloud Monitoring alerts for scheduled-query row-count metrics. Treat row count as an operational signal, not proof that the results or citations are correct. See the scheduled-query alert documentation.
Monitor completion and errors as well as row counts. An empty result can indicate a broken upstream process or an unexpected sample change, while an expected row count cannot validate the contents.
Gemini in BigQuery is optional
You do not need Gemini in BigQuery to run this analyzer: BigQuery can execute the SQL directly. Gemini in BigQuery is an optional analyst-assistance feature that can help explain or generate SQL and analyze datasets. Setup involves enabling APIs and granting roles; Google also warns that Gemini in BigQuery does not support all BigQuery compliance and security offerings. Check the project’s requirements before enabling it. Details are in Google’s Gemini in BigQuery setup documentation.
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