How to Audit Your Site for AI Extractability

How to Audit Your Site for AI Extractability

ByAdam

AI search systems extract and cite content differently from traditional search crawlers. Learn how to audit your site to ensure AI can find, read, and cite your content.

Traditional SEO audits check whether search engines can crawl, index, and rank your content. An AI extractability audit checks a different — but related — set of questions: whether AI search systems can find your content, understand what it’s about, extract the specific answers it contains, and attribute those answers to your site in a generated response.

The two audits share most of their technical foundations (crawlability, indexation, structured data) but diverge on content structure, answer clarity, and entity signals. Here’s a systematic, four-step framework for auditing both dimensions in a single pass, plus a worked example of what fixing the gaps actually looks like.

The Short Version

An AI extractability audit covers four layers: crawlability and indexation (can AI systems find the page at all), structured data coverage (FAQPage, Article, Organization, Person schema), content structure (answer-first headings, self-contained sections), and entity signals (consistent brand naming and credentialed authorship). Most sites that are well-optimised for traditional SEO are 70% of the way there already — the gap is almost always in content structure and entity consistency, not crawlability.

StepWhat it checksPrimary tool
1. Crawlability & indexationCan AI systems reach and index the pageSearch Console Coverage report
2. Structured data coverageIs the page machine-readable as a content typeGoogle Rich Results Test
3. Content structureIs the answer extractable without reading the whole pageManual review against the checklist
4. Entity signalsIs the brand and author identity consistent everywhereManual cross-site audit

Step 1: Crawlability and Indexation

AI Overviews and AI search tools that cite live web content (Perplexity, ChatGPT with search) draw from indexed pages. The first check is whether your key pages are indexed. In Search Console: Coverage report → Indexed pages. Any page you want to appear in AI citations must be indexed. Common issues that block indexation: noindex meta tags applied incorrectly, canonicals pointing to a different URL, pages blocked by robots.txt, pages not linked from the rest of the site (orphan pages), or thin content that Google has determined isn’t worth indexing.

Also check whether key pages are included in your sitemap and whether the sitemap is submitted to Search Console. AI systems that crawl the web use sitemaps as a starting point for discovery — a comprehensive, up-to-date sitemap ensures all your content is discoverable.

Step 2: Structured Data Coverage

Check structured data implementation using Google’s Rich Results Test (search.google.com/test/rich-results) on your key pages. For AI extractability, the most important schema types to audit:

  • Organization or LocalBusiness schema on the homepage: Establishes the entity identity of the site.
  • Article schema on blog posts and guides: Signals that the page is a structured piece of content with an author, a publication date, and a defined topic.
  • FAQPage schema on pages with Q&A sections: Makes question-and-answer content machine-readable and more likely to be extracted by AI systems.
  • Person schema on author bio pages: Establishes the author as an entity with expertise in a topic area — directly relevant to E-E-A-T and AI citation.

Step 3: Content Structure Audit

For each key page, check whether answers are clearly extractable by reviewing:

  • Does the H1 clearly state the page’s topic? AI systems use heading structure to understand content organisation. A H1 that says “Thoughts on Technical SEO” is less useful than “Technical SEO for Ecommerce: Faceted Navigation, Pagination and Duplicate URLs.”
  • Do H2 headings match the questions the page addresses? H2s that are phrased as questions or clear topic statements are more extractable than creative, vague, or punny headings.
  • Does the first 150 words contain the key answer or summary? AI systems prioritise content that leads with the answer, not content that builds to it after a long preamble.
  • Are there distinct, self-contained sections? A page structured as flowing prose is harder to extract from than a page with clearly delineated sections that can each be cited independently.

Step 4: Entity Signal Audit

Check whether your brand is consistently named across your site (consistent entity name in all schema, footers, and author bios), whether your author pages include professional credentials and external profile links, and whether your domain name appears consistently on external platforms where AI systems may encounter it (Google Business Profile, LinkedIn, Crunchbase, industry directories). Inconsistencies in brand naming or entity presentation fragment the authority signal that AI systems use to evaluate source credibility.

70%
Of an AI extractability audit overlaps with a standard SEO audit
150
Words — where the key answer should land at the latest
4
Schema types to prioritise: Organization, Article, FAQPage, Person

For structured data implementation details, see schema markup for AI visibility. For entity SEO specifically, see entity SEO fundamentals.

A Worked Example

A 200-page B2B services site ran this four-step audit across its top 30 pages by traffic. Crawlability and indexation passed cleanly — no surprises there. Structured data was the first real gap: 12 of the 30 pages had no Article schema at all, and 4 pages had FAQ sections with no FAQPage markup despite having genuine Q&A content.

The content structure step found a bigger issue: across the 30 pages, the average distance from the H1 to the first direct answer was 280 words — almost double the recommended threshold — because every page opened with a multi-paragraph scene-setting introduction before getting to the point. The entity audit found three different formal names for the company in use across the homepage schema, the footer, and the author bios (a legal entity name, a trading name, and an abbreviated version).

The team prioritised fixes in order: standardised the entity name everywhere first (a one-day fix), added missing FAQPage and Article schema over two weeks, then rewrote openings on the 30 pages to land the answer within the first 100 words. Within the following quarter, AI Overview citation tracking (checked manually against a list of 60 target queries) rose from 3 cited queries to 19 — without any change to organic rank, backlinks, or new content production.

Get Your AI Extractability Audit

Frequently Asked Questions

A regular SEO audit focuses on ranking signals: crawlability, indexation, page speed, keyword targeting, backlink profile, and on-page optimisation for traditional organic rankings. An AI extractability audit adds a layer focused on how AI systems understand and use content: whether answers are directly extractable (answer-first structure), whether structured data helps machines identify content type and topic, whether entity signals are consistent and strong enough to establish domain authority in the AI’s training or retrieval context, and whether the content covers questions comprehensively enough to be preferred as a primary source. Most of a regular SEO audit also applies to AI extractability — the two audits share 70% of their checklist, with the AI-specific checks adding the remaining 30%.

For traditional organic rankings, yes — Core Web Vitals and page speed are ranking factors. For AI citation specifically, the effect is less direct. AI systems crawl pages to extract content; if a page loads slowly or requires JavaScript execution that the crawler doesn’t complete, the content may not be fully extracted. Sites that serve content in server-rendered HTML (visible without JavaScript execution) are more reliably crawled than sites that depend on client-side rendering to display their main content. This is the same concern that affects traditional Googlebot crawling — JavaScript rendering has always been a crawlability risk — and it applies to AI crawlers as well.

This is a strategic decision that depends on whether you see AI training as a benefit or a risk. Blocking AI training crawlers (OAI-SearchBot for OpenAI, GPTBot, and others via robots.txt) prevents your content from being used in AI model training data but doesn’t affect live AI search citation (which uses the standard Googlebot crawl). If you want to appear in AI-generated answers but don’t want your content used in AI training, you can block training-specific crawlers while keeping your content accessible to live search AI systems. Some publishers block training crawlers as a principle; others see training data inclusion as a source of brand exposure. Neither position is wrong — but it’s a decision worth making deliberately rather than by default.

Three practical tests: (1) Ask ChatGPT (with web search enabled) or Perplexity to answer a question that your page directly addresses, and check whether your page is cited. If it isn’t, try the same question with your site name included (“what does 80AM Digital say about technical SEO for ecommerce?”) — if it can answer with attribution to your page now but not without the brand name, your page isn’t well-established as an authority on the topic. (2) Use Google’s Rich Results Test to check structured data; errors or missing schema reduce extractability. (3) View your page source (Ctrl+U in Chrome) and check whether the main content is present in the HTML source — if it only appears after JavaScript runs, AI crawlers without JS execution may not see it.

A full four-step audit across key pages once or twice a year is reasonable for most sites, with a lighter spot-check after any major site change: a redesign, a CMS or page-builder migration, a new templating system, or a significant content restructuring. Structured data in particular is fragile — it can break silently during a template change with no visible symptom on the rendered page, so re-validating schema after any development work touching templates is worth doing as a standing checklist item rather than waiting for the next scheduled audit.

Start with a prioritised subset — typically your top 20–50 pages by organic traffic or commercial value — rather than attempting a full-site audit on the first pass. This surfaces the patterns (a missing schema type, a structural habit like long preambles) that are likely repeated site-wide, which you can then fix at the template level rather than page by page. Reserve a full-site crawl-based audit for sites under 500 pages where it’s practical, or for the highest-value sections of larger sites.

Make Your Site Easy for Machines to Read

AI extractability and traditional SEO aren’t in tension — the same foundations that make a site crawlable and rankable also make it more extractable by AI systems. The additional work is in content structure: leading with answers, using descriptive headings, marking up Q&A content with schema, and establishing consistent entity signals. These aren’t radical changes for a well-optimised site — they’re refinements that make the machine-readability properties already there more reliable and explicit.

If you’d like a full AI extractability and SEO audit of your site, get in touch.

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