A clear scope
What MONITORING means here
AI visibility monitoring combines available native reports with a documented observation panel.
AI Visibility Monitoring owns the specific workflow and deliverables described below; related pages supply complementary work and should not duplicate its scope.
The buyer problem
Where the work makes a difference
One person sees a brand mention while another sees a different answer. Without a stable question sample, dates and source records, screenshots are easy to mistake for an overall visibility trend.
Track citations, mentions, factual accuracy and identifiable referrals as different signals. Keep the sample stable enough to learn from, and be explicit about the interfaces, countries and questions it represents.
The specific work
What we investigate and improve
- Set up a bounded prompt and buying-scenario panel with repeat observations.
- Capture source URLs, model/interface, search mode, locale and date.
- Review supported Search Console AI impression and Bing citation reports.
- Link identifiable referral behavior to ecommerce analytics without inventing unseen demand.
BrownBag’s approach
From evidence to useful improvements
Define a repeatable observation panel
Select buying scenarios, interfaces, countries and languages. Record the prompt, date, search mode and available model context, then agree separate fields for brand presence, linked citations and factual accuracy.
Combine available evidence
Review native Google generative-AI impressions and supported Bing citation reporting where the accounts provide data. Keep those figures separate from sampled answers and identifiable referrals so differing definitions remain visible.
Report changes with uncertainty
Repeat the agreed sample and compare observations alongside source-page changes. Flag factual errors and useful next actions, while distinguishing consistent findings from one-off variation and incomplete attribution.
Your handover
What you receive
- Measurement methodology and baseline.
- Native-report dashboard with data definitions.
- Prompt-sample evidence archive and accuracy rubric.
- Regular insight report with prioritized actions and uncertainty notes.
Evidence, with context
How we measure progress
- Native AI impressions where available
- Supported Bing citation activity
- Sampled mention/citation rates with denominators
- Observed factual accuracy
- Identifiable AI referral quality
Scope your next step
Bring us the problem you want to solve.
These details help us agree the deliverables, responsibilities and review points:
- Priority products, buying scenarios, countries and languages
- Available Search Console, Bing and analytics access
- Agreed platforms, repeat frequency and verified brand facts
Start with a focused scope. We confirm access, implementation ownership and any platform eligibility before the work begins.
Your questions, answered
Does Google provide separate AI reporting?
Search Console provides dedicated generative-AI impression reports. Visibility in an individual property depends in part on sufficient data. We review the available report alongside other evidence and document gaps.
Does that give separate AI revenue?
No. The cited report documents impressions, not a complete AI-to-revenue attribution chain.
Is a 40% mention rate our market share?
No. It means the brand appeared in 40% of the defined observations. It is not a measure of all real user questions.
Can all platforms be compared directly?
Only with careful qualification. Native metrics and sampled interfaces have different coverage and definitions.