A GEO (generative engine optimization) audit measures whether an AI answer engine can fetch your page, extract a self-contained answer from it, identify who you are, and trust the claim enough to cite you. It is a retrieval and attribution audit, not a ranking audit.

What is the difference between GEO, AEO and SGE optimisation?

They describe the same work from different angles. AEO (answer engine optimization) emphasises answering the question directly. SGE optimisation referred specifically to Google's AI results, now shipped as AI Overviews. GEO is the broader term covering every generative engine: ChatGPT, Perplexity, Gemini, Claude, and Copilot.

Which checks matter most for AI search visibility?

In order: AI crawler access in robots.txt, server-rendered content, a direct answer in the opening passage, entity clarity through Organization or Person schema with sameAs links, and sourced statistics. Most sites fail on the first two without knowing it.

Is there a free tool that runs a GEO audit?

Yes. IMVASA Audit Tool runs this GEO checklist alongside a full technical SEO audit on any public URL, free and without signup, and shows the exact passages an AI engine is most likely to quote.

Does llms.txt actually help AI search visibility?

It does not affect Google ranking. It gives AI crawlers and agents a curated map of your site, which mainly helps large or documentation-heavy sites. It is cheap to add and it is the file most often missing when a GEO audit runs.

GEO / AEO audit checklist

Generative engine optimization (GEO) audit checklist

A GEO audit checks whether AI answer engines can fetch your page, lift a self-contained answer from it, work out who you are, and trust you enough to cite you. This is the full checklist, 42 items across 6 groups, in the order that actually breaks: crawler access first, then answer readiness, then entity and evidence.

Written by Vasa, GEO & AEO specialist · Last updated August 29, 2026

Why GEO is not just SEO

Classic SEO competes for a ranked link, so it optimises the page as a whole. Generative engines retrieve and quote fragments, so they reward pages made of self-contained, attributable passages. A page can rank on page one and still never be cited, usually because its answer is spread across six paragraphs, its crawler access is blocked for AI user agents, or its content only exists after JavaScript runs.

AI crawler access

An answer engine can only cite what it can fetch. This is the single most common reason a good page never appears in AI answers.

GPTBot and OAI-SearchBot access: Whether robots.txt allows OpenAI's crawler and search fetcher on this path.
PerplexityBot access: Perplexity crawls and cites separately from Google.
ClaudeBot and anthropic-ai access: Anthropic's crawlers evaluated against your rules.
Google-Extended: Controls Gemini and AI Overviews grounding independently of Googlebot.
Applebot-Extended and CCBot: Two more retrieval paths most sites never check.
Blanket AI blocks flagged: A wildcard disallow that silently removes you from every answer engine.
Server HTML contains the answer: Client-rendered content is frequently invisible to AI fetchers.
Content-type and encoding: Served as parseable HTML with a declared charset.

Answer readiness

AI engines lift self-contained passages. This group scores whether such passages exist on your page at all.

Answer-ready passages detected: The tool extracts the passages most likely to be quoted and shows them to you.
Direct answer in the opening: The core question is answered within the first 40 to 60 words.
Question-shaped headings: Headings phrased the way people actually ask.
Self-contained paragraphs: Passages that still make sense with all surrounding context removed.
Definition sentences: 'X is …' constructions models reuse verbatim.
Lists and tables for enumerable answers: Structured formats are extracted far more reliably than prose.
No answer buried below the fold: Depth of the first substantive answer in the document.
Pronoun and context dependence: Passages leaning on 'it' and 'this' cannot be quoted standalone.

Chunkability and structure

Retrieval works on chunks. Badly structured pages chunk badly and lose to worse content.

Section length distribution: Sections short enough to survive chunking, long enough to be complete.
Heading density: Enough headings that each chunk carries its own topical label.
Semantic HTML sectioning: article, section and main instead of nested divs.
Heading hierarchy integrity: Skipped levels break the chunk-to-topic mapping.
Boilerplate ratio: How much of the page is nav and footer rather than content.
Table of contents: An in-page TOC helps both users and retrieval systems.

Entity clarity

Models must know who you are before they will attribute an answer to you.

Primary entity is named consistently: One canonical name, not five variations across the page.
Organization or Person schema: The entity exists in structured data, not just prose.
sameAs profile links: Profiles that let a model disambiguate you from similarly named entities.
Entity mentioned in the opening: Attribution depends on the brand appearing near the answer.
Product and service entities defined: What you offer, stated in plain language a model can repeat.
Consistent naming across schema and copy: Drift between markup and text weakens the entity.

Evidence and trust

Answer engines prefer sources that look verifiable. These are the signals they can see.

Statistics present: Concrete numbers are the most-quoted content type in AI answers.
Statistics carry sources: An unsourced number is a liability, not an asset.
Outbound citations to primary sources: Links to the origin of each claim.
Citation domain quality: Authoritative sources versus self-referential loops.
Named author with credentials: First-hand experience stated explicitly.
Published and modified dates: Freshness is a hard filter for many AI retrieval systems.
Date consistency: Visible dates matching schema dates.
Original data or first-hand experience: Content that cannot be paraphrased from elsewhere.

AI-native files and interfaces

The emerging layer. Very few sites have any of it, which makes it cheap differentiation.

llms.txt present and valid: Structure validated against the llmstxt.org proposal.
llms.txt content quality: Real page inventory rather than a marketing paragraph.
llms-full.txt where appropriate: Full-content variant for documentation-heavy sites.
MCP or WebMCP exposure: Whether assistants can call your site as a tool, not just read it.
Machine-readable feeds: RSS, JSON feeds or an API a model can follow.
Schema coverage for AI parsing: FAQPage, HowTo, Article and Organization where they genuinely apply.

Run the GEO checklist on your page

The audit reports AI crawler access per user agent, extracts the passages most likely to be quoted, and scores entity and evidence signals.

Frequently asked questions

What is a GEO audit?: A GEO (generative engine optimization) audit measures whether an AI answer engine can fetch your page, extract a self-contained answer from it, identify who you are, and trust the claim enough to cite you. It is a retrieval and attribution audit, not a ranking audit.
What is the difference between GEO, AEO and SGE optimisation?: They describe the same work from different angles. AEO (answer engine optimization) emphasises answering the question directly. SGE optimisation referred specifically to Google's AI results, now shipped as AI Overviews. GEO is the broader term covering every generative engine: ChatGPT, Perplexity, Gemini, Claude, and Copilot.
Which checks matter most for AI search visibility?: In order: AI crawler access in robots.txt, server-rendered content, a direct answer in the opening passage, entity clarity through Organization or Person schema with sameAs links, and sourced statistics. Most sites fail on the first two without knowing it.
Is there a free tool that runs a GEO audit?: Yes. IMVASA Audit Tool runs this GEO checklist alongside a full technical SEO audit on any public URL, free and without signup, and shows the exact passages an AI engine is most likely to quote.
Does llms.txt actually help AI search visibility?: It does not affect Google ranking. It gives AI crawlers and agents a curated map of your site, which mainly helps large or documentation-heavy sites. It is cheap to add and it is the file most often missing when a GEO audit runs.

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