How to check AI visibility: keep reviewable brand mention and citation records

An AI visibility check can begin with a specific question: in this answer, on this product interface, was the brand mentioned, was its description accurate and was there an inspectable source link? Recording these separately helps a team decide whether to correct information, add useful content or keep observing.

The following process is an editorial proposal for teams to adapt. It is neither a validated market survey nor a client case. Its purpose is to let a colleague reread an answer and understand how the record was classified.

Fix the questions and observation conditions

Start with actual buyer decisions. For example: "What data-permission conditions should an enterprise check before selecting a knowledge-search service?" This is an illustrative question, not a reported platform test. Group unbranded needs, named-brand fact checks and brand comparisons separately rather than combining them into one mention rate.

Keep each question's exact text, language, intended reader and version. Record the product and interface: ordinary conversation, search mode or an API experiment. Preserve the model or version if the interface exposes it; do not guess. Record login state, region, time, whether the conversation is new or continuing, and visible search or personalization settings.

Choose question count and frequency according to the decision, budget and capacity to review records. Complete a manageable sample before expanding it. There is no universal question count or two-week improvement threshold.

Preserve six groups of information for each answer

Check a citation's landing page and relevant passage, recording redirects, paywalls or inaccessible pages. A displayed link establishes that this answer presented that source. It does not reveal the model's full training data, hidden retrieval results or a universal preference for a type of source.

Repeats reveal variation, not causation by themselves

Repeat under comparable conditions and retain every result rather than keeping only the best screenshot. Keep a stable core question set and report additions separately. Annotate changes in platform features or settings. An absence in the sample describes that observation, not a lack of exposure to every user.

For follow-up questions, preserve the same conversation's preceding turns and order. A fresh conversation is not a continuation of the same user's journey. Automation can reduce transcription work, but should use the platform's supported access methods, retain execution errors and validate classification rules.

A change after a website revision is worth tracking. It may also reflect platform updates, question changes, other published material or observation conditions. Record it as a change to investigate rather than proof that one article increased citations.

Compare with platform reports and choose the next action

As checked on October 12, 2026, Google provides dedicated generative AI performance reports for visibility in supported features. Their impression data has a different denominator from a manual question sample. Bing AI Performance describes its data as sampled and aggregated, not a complete citation log. These can supplement observations; combining them does not establish market share across every AI product.

If a product description is wrong, check the website and inspectable references first. If a buyer question lacks a reliable answer, provide useful explanation or original evidence. Google's current AI search guidance continues to emphasize discoverable, useful content, without guaranteeing indexing or display. Avoid mass-producing near-identical pages for every question or seeking inauthentic third-party mentions.

Report what was observed, what remains unknown and the next action that can be checked. Then use GEO metric definitions and evidence to determine which measures are available and which require analytics or CRM data. To make content and measurement part of ongoing delivery, see FDM services. GEO addresses a visibility problem that an engagement may cover; FDM describes a collaboration and delivery approach. They are not synonyms.