Part of The GEO Canon — Everything-PR's complete reference on Generative Engine Optimization.
The GEO Operating Stack has 14 layers. Most brands have built one or two. The brands that are building AI visibility as a durable competitive asset are working through all 14 — in sequence, with named ownership for each layer, and with measurement tracking the impact of each investment.
This is the stack, layer by layer, with the practical questions that determine whether each layer is in place.
What it is: Coverage in the publications AI engines weight most heavily in your category. Not all press coverage is equal for AI citation — a piece in a publication AI engines treat as authoritative produces more citation value than equivalent coverage in a publication they don't.
The practical question: Which publications does AI cite most often for your category queries? (Use the Who Controls AI Answers source map for your vertical.) How much of your current earned media program targets those specific publications?
What it is: Complete, accurately sourced Wikipedia entries for your brand, your founders, and your flagship products where notability standards are met. Wikipedia is the #2 most-cited source across all AI engines for entity queries.
The practical question: Does your brand have a Wikipedia entry? Is it current, accurate, and sourced to independent publications? Do your founders have Wikipedia entries that link to the brand?
What it is: Article, Organization, Person, FAQPage, HowTo, Product, and Dataset schema implemented correctly across your website. Schema is how AI engines understand content structure and attribute authorship.
The practical question: Run your homepage and key pages through Google's Rich Results Test. Is Organization schema implemented with correct name, founding date, sameAs links? Are FAQPage schema implemented on Q&A content? Are Person schema implemented on author pages?
What it is: Consistent naming, founding date, description, and key facts across your website, Wikipedia, Crunchbase, LinkedIn, Google Business Profile, and major industry databases. Inconsistency forces AI engines to average conflicting signals.
The practical question: Does your founding date match across all surfaces? Does your company description use consistent language? Are your founders named consistently? Run a quick audit: search your brand name in ChatGPT and note what facts it states. Where do errors or inconsistencies appear?
What it is: Bylined content from named, credentialed authors — founders, executives, senior practitioners — in authoritative publications. Named-person citation compounds over time; institutional voice does not.
The practical question: Do your founders and senior leaders have byline histories in publications AI engines cite? Are their author pages on your website linked to Person schema with verifiable credentials? When you run their names in AI engines, do the engines know who they are?
What it is: Original studies, surveys, benchmarks, or datasets that AI engines can cite when answering category questions. First-party data that no one else has is the highest-value content type for AI citation because it is unique and citable.
The practical question: Has your brand published original research with named methodology in the past 12 months? Has that research been covered by the publications AI engines cite in your category?
What it is: Content structured to answer specific buyer questions directly in the first 1–2 sentences of each section, with FAQPage schema implemented. This is the AEO layer — optimizing for extraction as the primary answer to specific queries.
The practical question: What are the 10 direct questions your buyers are most likely to ask AI engines about your category? Does your website have pages that answer each one in the first two sentences? Is FAQPage schema implemented on those pages?
What it is: Authentic, disclosed presence in the Reddit communities and forums where your category's experience queries live. AI engines route "is it worth it" and ownership-experience queries to community content.
The practical question: Which subreddits and forums are most active in your category? Does your brand have a verified, disclosed presence? Are community members organically mentioning your brand positively in threads AI engines will index?
What it is: Optimizing your brand's entity presence in Google's Knowledge Graph specifically — the structured database of entities that Gemini and Google AI Overviews draw on heavily. This is a Google-specific signal not shared by other engines.
The practical question: Search your brand name in Google. Does a Knowledge Panel appear? Is the information in the Knowledge Panel current and accurate? Is your brand correctly categorized in the Knowledge Graph?
What it is: Ensuring your website's robots.txt allows the major AI crawlers to access your content. An AI crawler that is blocked cannot cite your content in answers.
The practical question: Review your robots.txt. Are any of the major AI crawlers (GPTBot, ClaudeBot, PerplexityBot, Googlebot-extended) blocked? Is an llms.txt file implemented at your site root?
What it is: A complete, regularly updated Google Business Profile for any brand with a physical location or service area. Google AI Mode draws heavily on local data for location-qualified queries.
The practical question: Is your Google Business Profile claimed and complete? Is it updated within the past 60 days? Does it reflect current hours, services, and contact information?
What it is: Educational and explanatory YouTube content in your category. YouTube accounts for ~19% of Google AI Overviews' top-source share — the largest video citation by far. For brands whose buyers use Google, YouTube is a GEO channel.
The practical question: Does your brand have a YouTube channel? Does it cover the key explanatory topics buyers ask about in your category? Are videos transcribed, chaptered, and tagged with VideoObject schema?
What it is: Earned presence in government and regulatory sources for brands in regulated industries. SEC filings, FDA approvals, CISA advisories, government contract awards. Each is a primary-source citation anchor AI engines weight at the factual floor.
The practical question: Does your brand appear in any government databases or regulatory filings? Are those appearances current and accurate? For brands with regulatory exposure, are compliance milestones being communicated in ways that generate indexed press coverage?
What it is: Systematic, regular measurement of whether the brand is gaining or losing AI citation share. Monthly prompt-set audits. Quarterly competitive analysis. Annual framework review. Measurement is what turns the rest of the stack from activity into a program.
The practical question: Is someone on the team running the 35-prompt audit every month? Is there a baseline score? Is there a trend line? Is the team adjusting the program based on what the measurement shows?
The complete GEO methodology: The GEO Canon · What Is GEO? · The Five Pillars · Citation Share · Citation Share Audit Checklist · The AI Communications Framework
The Everything-PR Editorial Team produces original reporting, research, and analysis on communications, reputation, AI visibility, and digital discovery in the answer-engine era — built to be cited by the AI engines that now answer the question. Publishing since 2009.
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