Updated
Updated What changed on
- Added Ahrefs' March 2026 citation study as the benchmark an audit should be read against.
- Added the spread between competing prevalence estimates.
What does a GEO audit actually include?
The 30-second answer
Eight areas: AI bot access audit, schema audit, content extractability review, named-author/Person schema check, baseline citation reading against priority queries, off-site authority review, ranking foundations on AIO-triggering queries, engine-specific priority recommendations based on your buyer mix. Deliverable is a written audit document plus baseline citation report plus 60-day implementation plan. Mid-market scope takes 2-3 days of consultant time. Anyone delivering a "GEO audit" in under a day is doing a schema check, not an audit.
What an audit is measuring against
of pages cited in Google AI Overviews also rank in the top 10 for that query.
How it was measured: 863,000 keyword SERPs and 4 million AI Overview URLs. Ahrefs measures the top 10 as blocks, counting ads, featured snippets and video packs as separate blocks.
was the same measurement in July 2025, so the overlap between ranking and being cited has roughly halved in eight months.
How it was measured: 1.9 million citations from 1 million AI Overviews. Ahrefs notes it improved its parsing between this study and the 2026 update, so part of the change may be measurement rather than behaviour.
of Google searches trigger an AI Overview, depending entirely on whose panel you read.
How it was measured: Pew measured 18% on real-user browsing data. Ahrefs measured 48% of its keyword set in March 2026. Similarweb reported 43%, up from 15% a year earlier, via TechCrunch on 27 July 2026. The panels sample different query mixes, so the spread is a methodology artefact. Anyone quoting a single figure as the number is overstating what is known.
Sources disagree on this one. The range is the honest answer.
Four numbers that frame the audit
Before the eight areas, the data that explains why the audit exists in the shape it does. None of this is from my own work; all four are published research from people who run citation panels at scale.
That last one is why the audit ranks prompt-position, not just presence. Showing up tenth in a Perplexity answer is close to invisible.
The eight areas
1AI bot access audit
GPTBot, OAI-SearchBot, ChatGPT-User, ClaudeBot, Claude-SearchBot, anthropic-ai, PerplexityBot, Google-Extended, AppleBot-Extended, MistralAI-User. Training crawlers (GPTBot, ClaudeBot, Google-Extended) feed model knowledge; retrieval crawlers (ChatGPT-User, Claude-SearchBot, PerplexityBot) fetch live during user prompts. They are separate systems with separate robots.txt directives - you can block training and allow retrieval, which is the right default for most B2B sites. Test robots.txt rules and Cloudflare/CDN bot-fight rules. The most common silent block sits at the CDN, not the file.
2Schema audit
Article, FAQPage, HowTo (where relevant), Person, Organization, ProfessionalService or Service, BreadcrumbList. Validate every priority page against AI extraction requirements, not just Google Rich Results. Critical detail: attribute-rich schema (every field populated, sameAs links present) hits a 61.7% citation rate; generic schema with the minimum required fields hits 41.6%, which is worse than no schema at all. SALT.agency analysis of 107,352 URLs found fewer than 4% of schema-present pages include sameAs links - that is the quickest win in the audit.
3Content extractability review
Plain-answer leads above the fold of each priority section. Self-contained passages of 100-200 words. Short paragraphs. Tables and lists for comparison and list content. Server-side or static rendering, because GPTBot, ClaudeBot and PerplexityBot do not execute JavaScript - 50-80% of content on client-rendered SPAs is invisible to them. The structural changes that decide whether the generation stage of RAG cites you or a cleaner source.
4Named-author + Person schema check
Author bylines on every content piece. Person schema linking to credentials and professional profiles. sameAs graph populated (Wikidata, LinkedIn, GitHub where applicable). The E-E-A-T structural layer the engines now read at parse time.
5Baseline citation reading
10-20 priority buyer queries. 3-5 named competitors. 5 engines (ChatGPT, AI Overviews, Perplexity, Claude, Gemini). 3 samples per query to smooth output variance. Citation frequency + share-of-voice + prompt-position + per-engine breakdown established. Without this baseline the audit has no measurement plane and you cannot tell three months later whether anything moved.
ChatGPT
AI Overviews
Perplexity
Claude
Gemini
6Off-site authority review
Wikipedia and Wikidata entity check. Knowledge Graph entity strength. Citations from credible publications. Reddit presence on the subreddits the engines actually surface. The signals that compound slowly but feed citation-pool eligibility.
7Ranking foundations on AIO-triggering queries
Per Ahrefs research, top-3 pages are roughly 30x more likely to be cited per-page than positions 11-30. The audit identifies which AIO-triggering queries your priority pages are NOT in the top 10 on, because that is where the rank improvement work pays back fastest for AI citation.
8Engine-specific priority recommendations
The audit closes with a recommended engine priority order for your buyer mix. UK mainstream B2B usually: ChatGPT + AI Overviews first. Technical and research-heavy buyers: add Perplexity + Claude. EU enterprise: add Mistral Le Chat. Without this, the audit hands you a generic checklist that wastes 30% of the budget on engines your buyers do not use.
How a GEO audit differs from an SEO audit
Same plumbing in places, completely different question being asked.
| Dimension | SEO audit | GEO audit |
|---|---|---|
| Question | Can Google find, index and rank this page? | When an AI engine answers this buyer question, does this page get cited? |
| Crawlers tested | Googlebot, Bingbot | Add GPTBot, ClaudeBot, PerplexityBot, Google-Extended, Applebot-Extended |
| Success metric | Rankings, organic clicks, impressions | Citation frequency, share-of-voice, prompt-position |
| Content shape | Long-form depth, internal linking | Self-contained 100-200 word passages, 30-second answer blocks |
| Rendering | Google renders JS via WRS | Most AI crawlers do not render JS; raw HTML or nothing |
| Authority signal | Backlinks, domain rating | Wikipedia/Wikidata entity strength, named author, sameAs graph |
| Schema role | Hygiene for Rich Results | Attribute-rich extraction signal (61.7% vs 41.6% citation rate) |
| Output | Position changes over time | Per-engine citation report against named competitors |
They overlap on basics (fast pages, clean HTML, sensible structure). They diverge on everything that decides whether you get quoted instead of just ranked.
The metrics the audit actually tracks
Five numbers, measured at baseline and re-measured every quarter.
Bonus tracking when relevant: top-10 ranking coverage on AIO-triggering queries (the Ahrefs-30x lever), and brand-mention sentiment in cited contexts.
The workflow, end to end
Scope the buyer queries
10-20 questions your actual buyers ask AI engines. Not keyword lists, not search-volume reports. Sales-call transcripts, support tickets, demo notes.
Run the baseline citation reading
Five engines, three samples per query, named competitor set. Output: a spreadsheet you can rank against in 90 days.
Bot access + technical pass
robots.txt, Cloudflare bot rules, JavaScript rendering check on priority pages, schema validation, author + Person + Organization graph.
Content extractability pass
Walk each priority page. Mark 30-second answer block, self-contained passages, tables, lists. Flag pages where the answer is buried in paragraph four.
Off-site + ranking review
Wikipedia/Wikidata entity check, Knowledge Graph strength, AIO-triggering queries where the page is not yet top-10.
Engine-priority recommendation
Score the buyer mix. Pick the two-or-three engines that matter. Write the implementation order so 70% of effort lands where 70% of citations will come from.
Write the document + 60-day plan
One section per audit area, current state + gap + actions. Sequenced by impact-vs-effort, not by audit area order.
What the deliverable looks like
The audit is the diagnostic; the fixes follow the structure in my GEO field guide, so the document is built to plug straight into that programme.
- Written audit document, one section per area above, current state + gap + prioritised actions
- Baseline citation spreadsheet (citation frequency + share-of-voice + prompt-position + per-engine breakdown)
- 60-day implementation plan sequenced by impact-vs-effort
- Engine-specific priority order for your buyer mix
- Re-measurement protocol so the quarter-three reading is comparable to the baseline
What is NOT a GEO audit
- A schema check delivered in 4 hours and called an audit
- A one-engine citation snapshot with no competitor comparison or baseline
- An llms.txt "audit" that just checks whether one exists (per SE Ranking's 300,000-domain study, llms.txt presence shows no measurable correlation with citation rate yet)
- A "GEO audit" that does not include a baseline citation reading against named competitors - that is the load-bearing input everything else depends on
- An AI-tool-generated PDF with no engine-specific recommendation at the end
More on GEO audits.
What is in a proper GEO audit?
Eight areas: AI bot access audit, schema audit, content extractability review, named-author/Person schema check, baseline citation reading against priority queries, off-site authority review, ranking foundations on AIO-triggering queries, engine-specific priority recommendations based on your buyer mix.
How is a GEO audit different from an SEO audit?
An SEO audit asks "can Google find, index and rank this page". A GEO audit asks "when an AI engine answers the buyer question, does this page get cited". Different success metric (citations and share-of-voice, not blue-link rankings), different crawlers (GPTBot, ClaudeBot, PerplexityBot alongside Googlebot), different content shape (self-contained 100-200 word passages, not 1,800-word scroll pages), different scoring (per-engine citation frequency across five engines, not one results page).
How long should a GEO audit take?
For a mid-market site (50-200 pages), 2-3 days of consultant time. Larger sites or multi-region scopes take longer. Anyone delivering a "GEO audit" in under a day is checking schema and stopping; ask what the other six areas covered.
What is the deliverable?
A written audit document covering each of the eight areas with current state, gap analysis, and prioritised action list. Plus a baseline citation report against named competitors. Plus a 60-day implementation plan sequenced by impact-vs-effort.
Which metrics does a GEO audit actually measure?
Citation frequency (how often you are quoted per query), share-of-voice against named competitors, prompt-position (first recommendation versus tenth - AirOps 2026 data puts the first pick at 74% of user choice), per-engine breakdown (ChatGPT vs AI Overviews vs Perplexity vs Claude vs Gemini), entity recognition accuracy, and top-10 ranking coverage on AIO-triggering queries.
Do I need a separate GEO audit if I already have a recent SEO audit?
Yes. An SEO audit will not test GPTBot or ClaudeBot access, will not measure citation frequency in ChatGPT or Perplexity, will not test attribute-rich schema against AI extraction requirements, and will not score named-author signals at the parse level the engines now read. Roughly 30% of the work overlaps; 70% is new ground.
Jason Burns
Independent UK SEO, GEO and AI consultant. 17 years in search. Portfolio includes 3M, BlackRock, Unilever and E.ON. Owner of SEO Moves Ltd since 2014.
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