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GEO / FAQ

Common questions about enterprise GEO.

These answers are distilled from the three GEO guides: whether to start GEO, why AI does not recognize your brand, and how to accept GEO work. Each answer stands on its own; the source guides carry the full reasoning.

Decision guide

Should your company do GEO?

Should our company do GEO, and when is it worth starting?

Check five business signals first: whether customers are turning to AI for ready-made answers, whether AI describes your brand accurately and consistently, whether you maintain a set of public facts someone owns, whether buying decisions depend on being in the candidate set, and whether content work has a question-and-retest loop. Score each 0–2: 0–3 means fix fundamentals first, 4–6 suits a small pilot, 7–10 justifies making GEO an ongoing practice. The stronger the signals, the more a small GEO baseline run is worth doing now.

When is it wrong to start GEO right away?

Hold off when positioning and service scope are still shifting, when nobody owns fact-checking and retesting, when public material is too thin yet the team expects AI to assemble the brand story on its own, when you only care about a single mention or ranking, or when a provider is expected to guarantee citations on a specific platform and convert mentions into revenue. GEO surfaces how these problems show up inside AI answers; it does not solve unclear products or untrustworthy material for you.

What should the first round look like if we decide to start?

It does not start with mass publishing. Pick one real customer decision and run: one real scenario → 5 core questions → a pre-action baseline → one action → a same-condition retest. Define the scenario; collect about five questions covering awareness, comparison, fit, and fact-checking; save complete AI answers and citations under comparable conditions as the baseline; then take exactly one verifiable action and retest against the same questions.

Source guide (in Chinese): “Should your company do GEO? Five business signals to check first”Background: Google AI Features documentation

Diagnosis guide

Why does AI not recognize your brand?

Why does AI still not recognize our brand despite all the articles we publish?

Debug through four gates. First, AI cannot see the content — robots rules or security policies block AI crawlers, or key information lives in images, PDFs, or pages behind login. Second, it sees but cannot identify the brand — the copy never states who you are, whom you serve, and what problem you solve, or the same fact has conflicting versions. Third, it recognizes but does not trust — key facts exist only on your own site, with no third-party corroboration or quotable specifics. Fourth, the content does not match the questions customers actually ask. Most teams are stuck at the first two gates, regardless of publishing volume.

How do we check whether AI can read and recognize the brand?

Take the page you most want cited and ask an AI to summarize that page, or inspect the page source to confirm the body text exists as plain text — if the summary fails and the source shows only images and scripts, the problem is accessibility. Then ask the AI directly “what does XX do, and whom does it serve?” and look at which sources it cites: if the brand’s key facts all come from third-party pages, your own site is not yet acting as the fact anchor.

Is there a diagnostic path we can run immediately?

Yes: 3–5 real questions → layered checks → content mapping → one focused action. Pick 3–5 questions customers actually ask, ask them on 2–3 common AI platforms, and save complete answers, cited sources, and query conditions; check the four gates in order; map published content against customer questions to see where the gap concentrates; then fix exactly one gap and retest with the same questions. AI answers fluctuate — never conclude from a single run.

Source guide (in Chinese): “You publish a lot — why does AI still not know your brand?”Reference: Google’s robots.txt documentation

Acceptance guide

How do you accept GEO work?

How should GEO work be accepted and reviewed?

Layer first, numbers second: valid collection, AI mentions, accurate understanding, AI citations, and making the candidate list are five different things. First confirm collection is valid — raw answers are auditable and collection failures are counted separately from genuine non-mentions; then check whether mentions are described correctly, whether your own pages appear as cited sources, and whether you enter shortlists on comparison questions. Business conversion is not GEO’s direct deliverable; any promise converting mention counts into revenue is itself a red flag.

Does a rising mention rate prove GEO is working?

Not on its own. Mentions do not distinguish right from wrong — repeated wrong claims also lift the mention rate while building negative equity for the brand. Review accurate understanding too (are category, scenarios, and capability boundaries stated correctly, with an error list and corrected facts?) and citations (does the answer’s source list include your pages, with links that open and verify?). Passing off “the brand was mentioned” as “our site was cited” is a common way numbers get inflated.

How do we avoid being misled by inflated metrics at acceptance?

Follow “agree on the method, check the layers, audit the raw records, then set the retest cadence”: before work starts, agree in writing on the question set and sampling conditions; when deliverables arrive, check which of the five layers they cover; randomly audit raw records so every number traces back to the original question, conditions, and full answer; then fix a retest cadence, because AI answers fluctuate and important conclusions need multiple same-condition observations.

Source guide (in Chinese): “How to accept GEO work: mentions, citations, and making the shortlist”

KEEP GOING

Put the method into a workflow.

Enterprise GEO GEO Lab Manual 01