The GEO Audit: What to Check and How to Fix It

At some point, reading about GEO has to give way to actually doing something about it. The right starting point isn’t a new content brief or a schema implementation plan. It’s a diagnostic: a structured look at where your brand actually stands in AI-generated answers before you touch anything.

That’s what a GEO audit is. It maps the specific gaps between where you are and where you need to be to appear consistently in AI-generated answers. Without it, GEO improvements are guesswork. With it, you have a prioritized list of fixes ordered by impact.

This post walks through the four components of a complete GEO audit: what to check in each, what you’re looking for, and what to do when you find a gap.

What a GEO Audit Is

A GEO audit is a structured review of the signals that determine whether AI systems cite your brand: covering citation performance, entity signals, schema markup, and content gaps.

It produces two things: a baseline (where you stand today, benchmarked against competitors) and a prioritized action plan (what to fix, in what order).

A GEO audit is not an SEO audit with extra steps. SEO audits examine page performance, crawlability, and keyword rankings. A GEO audit examines brand entity recognition, schema completeness, and how your content performs when AI systems generate answers. Some of the inputs overlap, but the outputs are measured differently, and the fixes are often different too.

The audit has four components. Most teams find gaps in at least two of them on the first pass.

Component 1: Citation Testing

Start here. Before auditing what might be causing a visibility problem, you need to know whether you have one.

Citation testing means running a set of buyer-intent queries across AI platforms and recording whether your brand appears in the answers. Pick ten questions your buyers would realistically ask an AI system when researching your category. Run them across ChatGPT, Perplexity, and Claude. For each response, note: did your brand appear, where in the response, was the description accurate, and which competitors were named.

What you’re looking for: your citation rate (how often you appear), your citation position (where in the answer), citation accuracy (whether the description is correct), and how those numbers compare to competitors across each platform.

A few patterns worth knowing from running these audits:

Brands absent from ChatGPT but present on Perplexity typically have weak entity signals but fresh, well-structured content. ChatGPT relies more heavily on training data and entity authority; Perplexity often pulls from live retrieval. The fix for each platform is different.

Brands present on both but described inaccurately have an entity consistency problem. The model has found them, but is pulling from inconsistent or outdated sources. That’s a different audit item than being absent entirely.

Brands absent from all three usually have either very weak entity signals or content that isn’t structured for extraction. The next two components will tell you which.

Run this component first. The citation data tells you what’s happening. The next three components tell you why.

A step-by-step framework for running the monthly citation test

Component 2: Entity Signal Review

Entity signals are the external profiles, database entries, and third-party mentions that allow AI systems to verify your brand independently of your own website.

AI systems don’t take your word for what your company does. They check external sources, cross-reference descriptions, and form a confidence level about your brand based on what independent sources say. A brand with complete, consistent entity signals gets cited confidently. A brand that only exists on its own website gets skipped.

The entity signal review checks for presence and completeness across six sources:

Wikipedia and Wikidata. Wikipedia is heavily weighted in ChatGPT’s training data. Wikidata is the structured, machine-readable companion that AI systems query directly to verify entity facts. Many companies don’t have either. If your category has notable players with Wikipedia entries and yours doesn’t, that’s a meaningful gap for ChatGPT specifically.

Google Business Profile. Google’s own systems (including AI Overviews) use this to confirm a business exists, its category, and its contact information. Missing or incomplete profiles leave a gap in Google’s entity graph.

G2 and Capterra. Review platforms are particularly influential for Perplexity, which retrieves live content and weights vendor comparison sources heavily. If you have no reviews or a sparse profile, Perplexity has less to work with when your category comes up.

Crunchbase and LinkedIn. Both are widely crawled and indexed. A Crunchbase entry with a consistent company description, industry classification, and website URL helps AI systems categorize your brand. LinkedIn’s company page signals the same things at scale.

For each source, you’re checking two things: does the profile exist, and is the description consistent with how you describe yourself on your own website? This is where most brands have more work than they expect. We regularly see companies listed as an “AI software platform” on one directory, a “data analytics company” on another, and something else entirely on LinkedIn. To a human reader, that’s just stale marketing copy. To an AI system trying to classify your brand, conflicting signals produce hedged, imprecise citations or no citation at all.

Inconsistency across sources (different category descriptions, different target customer language, outdated positioning) reduces the confidence AI systems have in describing you, even if all the profiles exist. Alignment matters as much as presence.

What entity signals are and why they determine AI citation

Component 3: Schema Markup Audit

Schema markup is machine-readable code on your website that tells AI crawlers and search engines exactly what your content means. The schema audit checks whether your site has implemented the types that matter most for GEO, and whether they’re implemented correctly.

Run your homepage and key content pages through Google’s Rich Results Test. Four schema types to check:

Organization schema on the homepage. This is the foundational entity signal on your own website. It declares your company name, URL, logo, and critically, links to your external profiles via sameAs properties. Without it, AI crawlers have to infer your identity from unstructured page content. In audits we run, a missing or incomplete Organization schema on the homepage is one of the most common gaps we find. It’s also one of the faster fixes: a developer task measured in hours, not weeks.

FAQPage schema on FAQ content. If you have FAQ content but no FAQPage JSON-LD, your Q&A pairs aren’t being communicated to AI systems in a structured way. They can still be read as prose, but the explicit machine-readable labeling of each question and answer is missing. This schema type is particularly high-impact for Google AI Overviews.

Article schema on blog and resource content. Signals authorship, publication date, and content category. Helps AI systems assess freshness and relevance.

BreadcrumbList on multi-level pages. Helps AI systems understand your site’s content hierarchy. This signals topical depth and organization.

Note what’s missing, note what’s implemented but broken (Rich Results Test will flag errors), and add it to the fix list. Schema improvements are among the fastest GEO wins available.

A full schema implementation checklist with JSON-LD examples

Component 4: Content Gap Analysis

The last component checks whether your site has the content types AI systems draw from most often when generating answers.

AI platforms don’t cite all content equally. They prefer content that directly answers questions, contains specific factual claims, and is structured in a way that’s easy to extract. The content gap analysis identifies which high-value types are missing from your site.

FAQ pages. The highest-value content type for AI citation. A well-structured FAQ page with direct answers and an FAQPage schema is one of the most consistently cited content assets across platforms.

Comparison pages. Buyers ask AI systems “how does X compare to Y?” constantly. If you don’t have content that addresses these comparisons specifically, you’re absent from a high-frequency query pattern.

How-to and implementation guides. Instructional content maps directly to how buyers phrase questions to AI systems. Numbered steps, clear headings, and direct answers at the start of each step are the structural requirements.

Case studies with quantified outcomes. Specific results (“reduced supplier costs by 23%,” “cut implementation time from six weeks to two”) give AI systems something concrete to cite. Vague case studies don’t perform as well because they don’t contain extractable facts.

Original research or proprietary data. AI systems actively seek out data they can’t find elsewhere. If you have internal data (survey results, benchmarks, analysis of your customer base), publishing it with clear attribution makes your content significantly more citable.

For each type, the audit answer is binary: present or not. If it’s not present, it goes into the action plan. If it’s present but not structured correctly (long narrative case studies without specific numbers, FAQ pages without schema), it goes into the fix list rather than the build list.

What AEO content structure looks like in practice

What to Do with the Results

Run all four components, and you’ll have a list of gaps. The next step is prioritizing them.

The sequencing that produces the fastest measurable results:

First: schema fixes. Organization schema on the homepage and FAQPage schema on existing FAQ content. These are developer tasks, typically hours of work, and their effect on AI systems that crawl live content (like Perplexity and Google AI Overviews) can show up within weeks.

Second: entity profile completion. Claim or update Google Business Profile, G2, Capterra, Crunchbase, and LinkedIn. Make sure the company description is consistent across all of them and matches your current positioning. This is the work most teams put off because it’s unglamorous, but it’s the foundation everything else depends on.

Third: content restructuring. Before creating new content, restructure what you have. Take your highest-traffic pages and apply the first-sentence test: does each H2 section lead with a direct answer? Add FAQ blocks where they’re missing. Reformat case studies to include specific metrics.

Fourth: content creation. Fill the gaps that can’t be fixed by restructuring: missing comparison pages, missing how-to guides, and case studies that don’t yet exist.

Fifth: longer-lead entity building. Press coverage, Wikidata entries, Wikipedia eligibility (where applicable), analyst mentions. These take longer to develop but produce the most durable improvements to AI citation confidence.

The logic behind this order: schema and profile fixes are fast and show results quickly, which gives you early data on what’s working. Content restructuring leverages what you already have before investing in new production. New content fills gaps that restructuring can’t solve. Long-lead entity building compounds over time.

Most companies that go through a GEO audit find that the first two phases cover a significant portion of their gap. The work is faster than most teams expect.