AI search visibility (GEO) — high-level pointers (local fallback)

chatot tells you whether a business is named, cited and ranked in AI answers. The detailed, current GEO standards — how to read a “named but never cited” gap, how to weigh the discovery funnel against brand/informational/comparison queries, and how to frame the two report variants for a client — live in the team wiki (set OUTLINE + OUTLINE_API_URL and the report links them directly). This file is the minimal high-level fallback for when the wiki isn’t configured: direction only, deliberately not step-by-step, so there’s no detailed content to drift out of sync with the wiki.

• Named but never cited — the classic gap; the fix is usually structured data / entity resolution, not more content, since the engine already recognizes the business.
• Low citation rate with real showing — presence is there, the link is thin; strengthen the citation-worthy content on the pages already being surfaced.
• Discovery gaps — a tracked search returning the business nowhere is a competitor’s win; prioritize by how close the query is to a real prospect question, not by volume alone.
• Site speed is not a visibility signal — keep it as hygiene, never let it drive the GEO verdict.
• There is no AI-specific optimization lever — Google’s own position is that eligibility for AI features is eligibility for ordinary Search; ground every recommendation in an existing on-page or off-page fix (indexability, crawlability, internal linking, structured data), never a fabricated “AI markup.”
• Non-determinism — a single AI answer is a sample, not a fact; read chatot’s numbers as a tracked baseline and trust the trend across cycles over any one run.
• Two variants — a retention report tracks progress against a prior baseline; a sales-lead report earns trust with a citable, ungimmicked assessment — never oversell either.

Full playbook → the team wiki’s GEO / AI-search-visibility standards.