Why You Need a GEO Audit
A GEO audit is the systematic process of evaluating how your brand appears — or fails to appear — across AI-powered search platforms. It is not the same as an SEO audit, although the two overlap in important ways. A traditional SEO audit assesses your visibility in Google’s organic results. A GEO audit assesses your visibility in the AI-generated answers that are increasingly replacing those results as the primary point of discovery.
The distinction matters because strong organic rankings do not guarantee AI visibility. We regularly audit sites that rank on page one for competitive terms yet are completely absent from ChatGPT, Perplexity and Google AI Overview responses for the same queries. The reverse also occurs: smaller, well-structured sites with clear entity signals get cited by AI platforms despite modest organic rankings. The signals that drive AI citation overlap with SEO signals but are not identical — and a GEO audit identifies exactly where the gaps are.
This checklist follows a seven-phase structure that mirrors the audit process we use for clients in our professional search visibility audit. You can work through each phase yourself using freely available tools, or use it as a framework to evaluate what a professional audit should cover. Either way, it gives you a clear, actionable picture of your current AI search visibility and what needs to change.
Phase 1: AI Crawler Access
Before anything else, you need to confirm that AI platforms can actually access your content. This is the equivalent of checking crawlability in a traditional SEO audit — except the crawlers are different and the rules are still evolving.
Start with your robots.txt file. Check whether you are blocking any of the major AI crawlers: GPTBot (OpenAI), ClaudeBot (Anthropic), PerplexityBot, Google-Extended (used for AI training), Bytespider (ByteDance) and CCBot (Common Crawl, which feeds many AI training datasets). Some businesses have blocked these crawlers — sometimes deliberately, sometimes through overly broad disallow rules — without realising the impact on their AI visibility. If GPTBot cannot crawl your site, ChatGPT cannot cite your content. It is that straightforward.
Next, check your server response to these user agents. Some CDN configurations and security tools block AI crawlers at the server level even when robots.txt allows them. Request your site with each major AI crawler user agent and confirm you get a 200 response with full content. Also verify that your content is not hidden behind JavaScript rendering that AI crawlers cannot execute — most AI crawlers have limited or no JavaScript rendering capability, so content loaded dynamically may be invisible to them.
Finally, review your meta robots tags and X-Robots-Tag headers. Directives like noai and noimageai are emerging standards that some publishers use to restrict AI usage. If these are present, they may be limiting your AI visibility intentionally or unintentionally.
Phase 2: Entity and Brand Verification
AI platforms make citation decisions based on entity recognition — how clearly they understand that your brand is a specific, defined entity with established expertise. This phase assesses whether AI systems recognise your brand at all, and whether they represent it accurately.
The most direct test is to ask. Query your brand name in ChatGPT, Perplexity, Gemini, Claude and Copilot. Ask: “What is [your brand]?”, “What does [your brand] do?”, “Who founded [your brand]?” Record what each platform says. Are the responses accurate? Complete? Or does the AI confuse you with another entity, provide outdated information, or return nothing at all?
Then test category queries — the questions your potential customers would actually ask. “Who are the best [your service] providers in [your area]?”, “What are the leading [your product] solutions for [your industry]?” Record whether your brand appears in these responses and in what position. This is your AI visibility baseline — the equivalent of checking your Google rankings, but for AI platforms.
Assess the consistency of your entity signals across the web. Is your brand name, description and expertise area consistent across your website, Google Business Profile, LinkedIn, Crunchbase, industry directories and Wikipedia (if applicable)? Inconsistencies confuse AI models. A brand described as “digital marketing agency” on its website but “web design company” on LinkedIn sends mixed signals about what entity this is and what it does.
Phase 3: Content Structure for AI
AI platforms extract and cite content differently from how Google indexes it. This phase evaluates whether your content is structured in a way that makes it easy for AI systems to retrieve, evaluate and cite.
Review your highest-priority pages through an AI extraction lens. Does each page have a clear hierarchical heading structure (H1 → H2 → H3) that communicates the topic architecture? Does each section answer its implicit question within the first one to two sentences, or is the key information buried in the third paragraph? AI retrieval systems typically extract passages, not full pages — if the most citable content is buried in a wall of text, it is less likely to be selected.
Check for what we call “citability markers” — specific, attributable facts, data points, definitions and frameworks that an AI can confidently reference. Pages that make vague claims (“we deliver excellent results”) are less citable than pages with specific evidence (“our audit identified 1,100 duplicate URLs across 146 blog posts, resolving cannibalisation that had suppressed rankings for 18 months”). AI systems preferentially cite sources that provide concrete, verifiable information.
Assess content freshness. Most AI retrieval systems weight recently published or updated content more heavily. Check the last-modified dates on your key pages and compare them to competing sources. If your cornerstone content has not been updated in 12 months but competitors refresh quarterly, you are at a retrieval disadvantage. Freshness signals must be substantive — changing a date without updating content is not effective and AI systems are increasingly able to detect superficial refreshes.
Phase 4: Structured Data Assessment
Structured data is the machine-readable layer that communicates your content’s meaning explicitly to search engines and AI systems. This phase evaluates whether your structured data implementation supports AI visibility.
Run your key pages through Google’s Rich Results Test and Schema.org’s validator. Check for the schema types that directly support AI extraction: Organisation (with complete sameAs links to your social profiles and directories), FAQPage (explicit question-answer pairs that AI systems can extract with high confidence), HowTo (step-by-step processes), Article (with proper author attribution linking to person entities), and Speakable (marking content suitable for voice and AI extraction).
Beyond presence, assess completeness. An Organisation schema that only includes your name and URL is far less useful than one that includes description, foundingDate, areaServed, knowsAbout, sameAs links and employee references. The more complete your structured data, the more confidently AI systems can identify, categorise and trust your entity. Pay particular attention to sameAs — these cross-references are how AI models connect your brand entity across platforms.
Check for schema errors and warnings. Invalid structured data is worse than no structured data — it sends confusing signals. Ensure all markup validates cleanly, that URLs in sameAs links resolve correctly, and that the information in your schema matches the visible content on the page. Discrepancies between schema claims and page content can undermine trust signals.
Phase 5: AI Brand Narrative Assessment
This phase goes beyond “are we mentioned?” to examine how AI platforms describe your brand — the narrative they construct. This is where many businesses discover uncomfortable gaps between how they position themselves and how AI systems represent them.
For each major AI platform, ask a range of questions that probe different dimensions of your brand narrative. Ask about your core expertise, your differentiators, your reputation, your leadership, your track record. Compare the AI-generated narrative to your intended positioning. Where does it align? Where does it diverge? Where is it simply absent?
Common findings include: AI platforms describing your brand using outdated positioning (“they used to focus on web design” when you have pivoted to AI optimisation), attributing expertise areas that are secondary rather than primary, omitting your key differentiators entirely, or confusing your brand with a similarly named entity. Each of these represents a specific remediation task — updating corroborating sources, strengthening on-site entity signals, or building new authoritative mentions that reinforce the correct narrative.
Pay particular attention to competitor mention co-occurrence. When AI platforms discuss your brand, which competitors do they mention alongside you? Are you grouped with the right peer set, or are you being categorised alongside lower-tier competitors? This co-occurrence pattern reveals how AI systems have categorised your entity relative to the competitive landscape.
Phase 6: Competitive AI Positioning
No audit is complete without understanding the competitive landscape. This phase evaluates where competitors are being cited and you are not — and what they are doing differently.
Identify your top 5–10 competitors and run the same AI platform queries you used for your own brand assessment. For each query where a competitor is cited and you are not, examine what the competitor’s cited source looks like. Is their content more comprehensive? Better structured? More recently updated? Do they have stronger entity signals or more complete structured data? This competitive gap analysis reveals the specific actions needed to close the visibility gap.
Build a citation matrix: a spreadsheet tracking 20–50 priority queries across 4–5 AI platforms, recording which brands are cited for each query on each platform. This matrix becomes your measurement baseline and reveals patterns — perhaps competitors dominate on Perplexity but you perform better in Google AI Overviews, or a specific content gap is costing you citations across all platforms. The matrix also exposes opportunities: queries where no competitor has strong AI visibility represent first-mover opportunities.
Look beyond surface-level citation presence. Examine the quality of competitor citations: are they cited as a primary source or a passing mention? Do AI platforms reference specific content assets (tools, research, data) from competitors that you lack? This deeper analysis reveals not just that you are losing to competitors, but why — and the “why” determines the remediation strategy. Sometimes the gap is content depth. Sometimes it is entity authority. Sometimes it is simply that a competitor published something structured and specific where you only have a vague service page.
Phase 7: Measurement Setup
A GEO audit is not a one-off exercise. This final phase establishes the monitoring infrastructure to track your AI visibility over time and measure the impact of changes you make.
Set up a monthly AI citation tracking cadence. Re-run your priority query set across all major AI platforms and update your citation matrix. Track changes over time — which queries have you gained citation for? Which have you lost? What changed? Monthly tracking is the minimum cadence; for fast-moving competitive landscapes, fortnightly is better.
Configure Google Search Console to monitor AI Overview performance, which Google now reports separately. Track referral traffic from AI platforms in your analytics — Perplexity, ChatGPT, Copilot and other AI platforms each have identifiable referral signatures. Establish conversion tracking for AI-referred traffic so you can measure not just visibility but commercial impact.
Document your baseline metrics before making any changes. You need a clear “before” picture to measure the “after” against. Key baseline metrics include: number of priority queries where you are cited (by platform), accuracy of brand narrative across platforms, entity recognition consistency, structured data coverage percentage, and content freshness scores versus competitors.
What the Checklist Covers — and What It Does Not
This checklist covers the assessments you can perform yourself with freely available tools and manual testing. It will give you a clear picture of your current AI visibility, the most obvious gaps, and where to prioritise your efforts.
What it does not cover is the deeper strategic layer that a professional search visibility audit adds: revenue exposure modelling (quantifying the commercial value of AI visibility gaps), competitive deep-dive analysis at scale, cross-platform citation pattern analysis, entity authority scoring against your specific competitive set, and a prioritised remediation roadmap with estimated impact. If the self-assessment reveals significant gaps — or if you want the confidence of knowing exactly what the gaps are costing you — that is where the professional audit earns its investment. Our AI visibility audit covers all seven phases at depth, with strategic recommendations tailored to your specific market position.
Ready to go deeper? Get in touch for a free initial consultation, or try our free search visibility score tool for an instant baseline assessment.
