Per our own AI-Overview audit of 1,026 buyer queries (June 2026), 879 returned an AI Overview and 233 of those cited FORKOFF.
FORKOFF · Tools
Paste a URL. Get a 6-dimension scorecard of AEO signals AI crawlers (Perplexity, Claude, ChatGPT-Bing) look for in the static HTML: schema density, FAQ structure, definitional opener, content-signal headers, meta + canonical, AEO-extra signals. Page-level audit, no LLM queries. Want per-brand citation share across LLMs instead? Use AI Search Visibility Checker.
<iframe
src="https://forkoff.xyz/tools/aeo-checker/embed"
width="100%"
height="1100"
frameborder="0"
loading="lazy"
title="AEO Checker by FORKOFF"
></iframe>Paste into a Custom HTML block.
Use /embed and paste the iframe src URL.
Drop into an Embed element. Lazy-loaded by default.
This checker fetches one URL and scores six dimensions read straight from the static HTML: schema density, FAQ structure, definitional opener, content-signal headers, meta and canonical, and AEO-extra signals (llms.txt, JSON-LD alternate link, HTTPS). No LLM is queried. It answers whether the page's markup is structured the way an AI crawler expects, not whether ChatGPT currently cites the brand. The single highest-leverage fix for most sites is structured data, which our guide to schema markup for AEO covers signal by signal. If you are still deciding which surface to optimize first, the AEO vs GEO guide draws the extraction-versus-generative line. To read live citation share across five LLMs instead of parsing one page's markup, run the AI visibility checker.
The score sums six weighted dimensions read from the page's static markup: schema density (25 pts), FAQ structure (20), definitional opener (20), content-signal headers (15), meta and canonical (10), and AEO-extra signals (10). No LLM is called. The checker returns the composite 0-100 score, an A-F grade, and the finding behind every point lost.
6 signals
Coverage
100 pts
Max composite score
3 points per JSON-LD block on the page (capped at 10), plus 2 points for every AEO-relevant schema type detected (FAQPage, Article, Organization, SoftwareApplication, Product, HowTo, WebPage, Service, LocalBusiness, VideoObject), capped at 15. FAQPage schema is the single strongest signal here.
Counts FAQPage schema Q&A pairs (up to 14 points) plus raw <details>/<dt> HTML patterns (up to 4 points), with a 2-point bonus at five or more total FAQ signals. Schema alone, HTML alone, or both all count.
Scans the first 200 characters of visible body text for a '{Brand} is {noun} for {ICP}' sentence. A full match scores 20, a partial 'X is...' without an ICP clause scores 10, no match scores 0. The cheapest fix on this checker.
Rewards exactly one H1 (7 points), two or more H2s (5 points), and any H3 (3 points). A hidden H1 (opacity-0 in the markup) is penalized 5 points, a pattern AI crawlers read as a manipulation attempt.
Title length (30-65 chars), meta description length (130-165 chars), and an HTTPS canonical tag score up to 10 points. A further 10 points come from a JSON-LD alternate link, an llms.txt / llms-full.txt reference, and HTTPS on the page itself.
Inputs · what the checker needs
A single page URL. The one input the audit needs. Every other field is optional.
Optional: audit after JavaScript render. For client-rendered SPAs where a raw static fetch returns an empty shell, this mode hydrates the page first before scoring.
Outputs · what the checker returns
Composite score and grade. A 0-100 score summing all six weighted dimensions (schema 25, FAQ 20, opener 20, headers 15, meta/canonical 10, AEO-extra 10), mapped to an A-F grade.
Six per-dimension scores with findings. Each dimension (schema density, FAQ structure, definitional opener, content-signal headers, meta and canonical, AEO-extra signals) reports its own score against its own max, plus a human-readable finding for every point gained or lost.
Remediation queue. One concrete fix per underperforming dimension, ordered by point value recoverable.
This AEO checker is a static-HTML structural audit. Paste one URL and it fetches the raw page markup, then scores six signals AI crawlers read to decide whether a page is worth citing: schema density, FAQ structure, a definitional opener sentence, content-signal headers, meta and canonical tags, and AEO-extra signals like llms.txt. No LLM is queried. It answers whether the page's markup gives an answer engine what it needs, not whether ChatGPT already cites the brand today. Those are different questions with different tools: for live citation share across ChatGPT, Perplexity, Claude, Gemini, and Grok, use the AI Search Visibility Checker instead.
Each card is a real AEO pattern the checker sees often. Single audits diagnose; quarterly cadences fix.
Scenario 01
A-Well-structured comparison page
A /compare/ page with FAQPage schema, a clear one-sentence definitional opener, and a clean H1-H2-H3 outline.
Composite score
88/100
Recommendation
Structurally sound. Hold this pattern on every new comparison page shipped.
Scenario 02
CLong-form blog post, thin structured data
A blog post with real prose depth and good headers, but no JSON-LD beyond the site-wide Organization schema, and no FAQPage block.
Composite score
54/100
Recommendation
Add FAQPage schema (up to 20 pts) and tighten the opening sentence into a direct definitional statement (up to 20 pts).
Scenario 03
BService page with rich schema, no FAQ
A /services/ page with Service and Organization JSON-LD, strong header structure, but zero FAQ content.
Composite score
71/100
Recommendation
Add an FAQ section with FAQPage schema, the single highest-leverage gap on this page type.
Scenario 04
DThin landing page, no structured data
A minimal launch page with hero copy and a CTA, no JSON-LD, no FAQ, no definitional sentence in the first 200 characters.
Composite score
12/100
Recommendation
Foundational gaps across every dimension. Add basic Organization or Service schema, an FAQ block, and a direct opening sentence before anything else.
Six dimensions · one static-HTML fetch
100 points. Zero LLM calls.
Every point in the composite score traces to a fixed, published rule across six dimensions: schema density, FAQ structure, a definitional opener, content-signal headers, meta and canonical, and AEO-extra signals. Fixing the lowest-scoring dimension first recovers the most points per unit of effort.
A schema validator checks syntax. A live citation checker queries an LLM. This tool sits between them: it scores whether a page's markup gives an AI crawler the structure it needs, without the cost or latency of a live LLM call.
← scroll horizontally to see more →
| Feature | FORKOFF AEO checkerSingle-URL static-HTML structure audit | AI Search Visibility CheckerLive multi-LLM citation audit (a different FORKOFF tool) | Classic SEO rank trackersAhrefs · Semrush · Moz | Manual schema validatorsGoogle Rich Results Test and similar |
|---|---|---|---|---|
| Scores structured data (JSON-LD schema types and density) | ||||
| Scores FAQ structure and FAQPage schema | partial | |||
| Scores a definitional opener sentence in body text | ||||
| Scores content-signal headers (H1/H2/H3 structure) | partial | |||
| Measures whether an LLM actually cites the brand today | ||||
| Requires no LLM calls, runs in under 10 seconds | ||||
| Best for tracking organic Google rank over time |
If your decision lands in the left column, the checker is the right surface. If it lands in the right, use a different tool.
Built for this
Not the right fit
It fetches one page's static HTML and scores six structural signals against fixed rules: schema density (up to 25 points), FAQ structure (up to 20), a definitional opener sentence in the first 200 characters of body text (up to 20), content-signal headers like H1/H2/H3 structure (up to 15), meta and canonical tags (up to 10), and AEO-extra signals like llms.txt and a JSON-LD alternate link (up to 10). No LLM is queried at any step.
No. This tool never queries an LLM, it only reads static HTML. For live citation share across ChatGPT, Perplexity, Claude, Gemini, and Grok, use the AI Search Visibility Checker instead, a separate FORKOFF tool built for exactly that question.
A schema validator checks whether your JSON-LD is syntactically valid. This checker goes further: it scores whether the RIGHT schema types are present for AEO purposes, whether FAQ content exists in either schema or HTML form, whether the page opens with a machine-extractable definitional sentence, and whether the header structure and meta tags support extraction. It is an AEO-readiness score, not a syntax validator.
It is a sentence early in the page's body text, checked in the first 200 characters, that states plainly what the brand or product is and who it is for, in a pattern like '{Brand} is {noun} for {ICP}'. Answer engines extract this kind of sentence directly when a user asks 'what is X'. A page that buries its definition three paragraphs down scores lower on this dimension even if the content is otherwise strong.
FAQPage schema and clearly marked question-and-answer HTML patterns (using <details>/<dt> or equivalent) give an answer engine a pre-formatted, directly citable question-and-answer pair. The checker counts both the schema entries and raw HTML FAQ patterns, since not every page uses structured schema for its FAQ content.
The checker counts JSON-LD blocks (3 points each, capped at 10) and gives a bonus (2 points each, up to 15) for AEO-relevant schema types specifically: FAQPage, Article, Organization, SoftwareApplication, Product, HowTo, WebPage, Service, LocalBusiness, and VideoObject, including types nested inside an @graph. Generic or off-topic schema types do not earn the bonus.
Yes, the scoring rules are page-type-agnostic; the same six dimensions apply to a blog post, a service page, a comparison page, or a landing page. What differs is which dimension is the realistic highest-leverage fix: a long-form blog post often has strong header structure already and is missing FAQ schema, while a thin landing page is often missing structured data entirely.
Under 10 seconds for a standard static-HTML fetch. Client-rendered single-page apps that need the JavaScript-render mode take longer, since the page has to hydrate before the checker can read the final markup.
Schema density and FAQ structure carry the two highest maximums (25 and 20 points), followed by the definitional opener (also 20). Content-signal headers (15) and meta/canonical plus AEO-extra (10 each) round out the composite. Fixing the lowest-scoring high-max dimension first usually recovers the most points per unit of effort.
This free single-URL checker exposes the same six-dimension scoring logic FORKOFF uses when auditing a page before a managed Answer Engine Optimization engagement. It is a fast, no-cost first pass; a managed engagement adds the live multi-LLM citation measurement (via the AI Search Visibility Checker) and an execution plan, not just a diagnosis.
Only if the page's markup changes. Because this is a static-HTML structural audit with fixed scoring rules, not a live LLM query, the score is stable across reruns unless you edit the page, add schema, or change the header structure in between.
Not on this page, it grades one URL per run. To compare several pages, run each one separately and read the per-dimension breakdowns side by side.
Yes, the single-URL static-HTML audit on this page is free with no signup. A managed FORKOFF AEO engagement builds on this diagnostic with live multi-LLM citation tracking and an execution plan; talk to a strategist if you want that.
A low composite score means the page's markup is missing structural signals an AI crawler can extract and cite, not that the page's content is bad. The remediation queue names the specific gaps: usually missing FAQPage schema, a buried or missing definitional sentence, or thin JSON-LD coverage. Fixing those is a markup change, not a content rewrite.
Sister tool. Audits a brand's surface across LLM-driven discovery for a custom commercial query set.
Post-level engagement audit for X. Same audit-ledger calibration logic, applied to engagement quality.
Sister tool. Audits the agent-readiness substrate (robots 7-bot allowlist, llms.txt, well-known manifests). The plumbing AEO sits on top of.
Generate the FAQPage, Article, Dataset, Organization schema that turns the AEO Checker recommendations into shippable code.
Composite scorecard combining AEO + GEO + Lighthouse into one unified 0-100 number.
Run a managed AEO program. Weekly audits + the foundation work that lifts a sub-30% brand to 65% inside two quarters.
Multi-LLM AEO scorecard · weekly cadence · sub-30% to 65% inside two quarters.
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