Why does the audit only weight LLM citation surface at 20% if it's the outcome metric?

Because citation surface is lagging — it reflects work done 30–90 days ago. Weighting it 100% would mean the audit can only diagnose pages that are already cited or already failing. The other five dimensions are leading indicators that move 4–8 weeks before the citation surface does, which makes the audit useful for pages that haven't been measured by LLMs yet.

What's the relationship between the AEO Checker tool and this AI SEO Audit tool?

The AEO Checker (/tools/aeo-checker) returns a 5-LLM scorecard for a brand + query set — citation share, snippet ownership, per-LLM presence. It's a brand-level surface measurement. The AI SEO Audit (this tool) is a single-page audit across six dimensions including LLM citation surface as one of them. Run AEO Checker for portfolio-level diagnosis; run AI SEO Audit for per-page fix-list.

Does this audit replace Ahrefs or SEMrush?

No. Ahrefs / SEMrush audit rank + link profile + crawl. The AI SEO Audit covers the AI-search layer those tools don't measure. The recommended stack: Ahrefs / SEMrush for ongoing rank + link monitoring, AI SEO Audit at T+0 / T+30 / T+60 / T+90 for AI-search surface, AEO Checker for portfolio-level brand citation share.

How accurate is the LLM citation surface dimension?

Median accuracy +/- 8 points against the FORKOFF managed-engagement ledger. LLMs re-weight on a 4–8-week cycle, which means citation surface can shift after the audit runs — pages that were uncited can become cited, and vice versa. Treat the score as a 30-day directional estimate, not a real-time measurement.

What does Tier-A / Tier-B / Tier-C / Tier-D mean?

Composite tier bands. Tier-A (75+) is cite-ready and defended. Tier-B (55–74) needs targeted lift on 1–2 dimensions. Tier-C (40–54) needs structural fixes on 2–3 dimensions. Tier-D (<40) is a rebuild candidate; restructure the page before re-auditing. The bands are calibrated against the FORKOFF managed corpus where Tier-A pages saw 3–9 LLM citations per query.

How quickly can a Tier-C page reach Tier-B?

30–45 days for the surface dimensions (schema, snippet, answer-engine, freshness). 60–90 days for the LLM citation surface to re-weight. Pages that fix all surface dimensions on day 1 typically see 12–22 composite-point lift by T+45 and another 6–14 by T+90 as the citation surface catches up.

Does the audit need a domain claim or analytics access?

No. The audit runs against the public page surface — schema markup, on-page content, FAQ + HowTo structure, last-updated timestamps — and against public LLM responses on the target query set. No GSC, no analytics, no signup. The managed engagement (FORKOFF AI Search Optimization) plugs into your CRM + GSC for closed-loop measurement.

Can I audit a competitor's page?

Yes. Same model. Run the same query set on a competitor URL to score the gap. The most valuable use is auditing the page that's currently ranking #1 (or being cited by ChatGPT) on your target query and using its dimension scores as the bar to clear.

What about non-English pages?

The audit works on any language LLMs cite — major Romance + Germanic + East Asian + Arabic. The query set should match the page's language. Per-LLM coverage varies by language (Perplexity is strongest at non-English; Grok is weakest); the composite weights are calibrated against English and may shift +/- 10% for other languages.

How does this connect to the FORKOFF AI Search Optimization engagement?

The audit is the kickoff artifact. Every FORKOFF AI Search Optimization engagement starts with a per-page audit across the top 20 commercial pages, which sets the baseline composite. The 90-day delivery program targets +20 composite points on the top 5 pages and +12 on the next 15. The free version exposes the model; the managed version applies it across a portfolio.

Is this just AEO with extra steps?

AEO is one of the six dimensions (snippet ownership). The audit covers the broader AI-search surface — Schema.org coverage, internal linking, answer-engine readiness, freshness + author signals — that AEO alone misses. AEO is a tactic; AI SEO is the page-level model.

Is the calculator free?

Yes. No signup, no email gate. Run as many pages as you want. The managed FORKOFF AI Search Optimization engagement applies the model to your portfolio with weekly reporting + closed-loop citation tracking.

FORKOFF · Tools

Does this page even show up in ChatGPT?

Q: What is an AI SEO audit, and why is it different from a regular SEO audit?

An AI SEO audit scores a page on its LLM-citation surface — whether ChatGPT, Perplexity, Claude, Gemini, and Grok cite the page on its target queries — across six AI-native dimensions (Schema.org coverage, AEO snippet ownership, LLM citation surface, internal linking, answer-engine readiness, freshness + author). A regular SEO audit (Ahrefs, SEMrush, Screaming Frog) scores rank position + link profile + crawl health. The two are complementary; a Tier-A AI SEO score on a Tier-C ranking page is rare and worth investigating, and vice versa.

Q: Where do the dimension weights come from?

Calibration corpus: 200+ pages audited inside FORKOFF's AI-search-optimization engagement, scored against actual LLM citation outcomes at T+30, T+60, T+90. AEO snippet ownership (22%) and LLM citation surface (20%) are the highest-weight dimensions because they correlate strongest with citation lift. Schema (18%) sets the baseline. Linking (12%) is the lowest because well-cited pages survive on weaker internal linking.

Q: How quickly can a Tier-C page reach Tier-B?

30–45 days for the surface dimensions (schema, snippet, answer-engine, freshness). 60–90 days for the LLM citation surface to re-weight. Pages that fix all surface dimensions on day 1 typically see 12–22 composite-point lift by T+45 and another 6–14 by T+90 as the citation surface catches up.

Q: Does the audit need a domain claim or analytics access?

No. The audit runs against the public page surface — schema markup, on-page content, FAQ + HowTo structure, last-updated timestamps — and against public LLM responses on the target query set. No GSC, no analytics, no signup. The managed engagement (FORKOFF AI Search Optimization) plugs into your CRM + GSC for closed-loop measurement.

Q: Can I audit a competitor's page?

Yes. Same model. Run the same query set on a competitor URL to score the gap. The most valuable use is auditing the page that's currently ranking #1 (or being cited by ChatGPT) on your target query and using its dimension scores as the bar to clear.

See what a 6-dimension AI-SEO audit looks like — Schema.org coverage, AEO snippet ownership, LLM citation surface, internal linking, answer-engine readiness, freshness + author signals — composite tier and the fix-list shape. The free tool returns the format; the managed engagement runs the actual fetch + LLM citation check across the portfolio.

Run the audit See the AI Search Optimization service →

How it works

Six dimensions decide whether ChatGPT cites the page.

The audit scores Schema.org coverage, AEO snippet ownership, LLM citation surface, internal linking, answer-engine readiness, and freshness + author signals, weighted to reflect what actually moves citations on the FORKOFF managed-engagement ledger.

Composite radar

All six dimensions in one polygon. Weak spokes drag the composite.

The weighted average is dragged down by any dimension under 40. Pulling the bottom dimensions into the 55+ band is the highest-leverage move; flagship dimensions above 75 hold the ceiling.

Tier mix

Median composite · un-optimized 2026 pages

200+

Pages in the calibration corpus

Dim 01-06per dimension

Per-dimension score bars.

0–100 per dimension. Anything below 50 is a fix priority. Anything above 75 is defended. The audit weight column anchors the composite calculation.

LLM5 engines × 4 queries

LLM citation grid.

Lit cell = page cited by that LLM on that query. Even one citation in the grid lifts the citation-surface dimension materially.

Schema@graph

Schema.org coverage stack.

Article + Org + BreadcrumbList present at baseline. FAQPage + HowTo + Author are the highest-leverage missing types. Each fills 4–8 composite points.

TrajectoryT+0 → T+90

Citation-lift curve.

Surface dimensions move at T+30. LLM citation surface catches up T+60 to T+90 as engines re-crawl + re-weight. Tier-A pages stay there with quarterly re-audits.

The model

Three inputs in. Three outputs out.

Inputs

Input 01

Page URL. The specific URL to audit. Audit scope is the single page + first-degree internal links — not a full domain crawl.

Input 02

Target query set. 5–15 commercial queries the page should rank + be cited on. The query set anchors the AEO snippet ownership + LLM citation surface dimensions.

Input 03

Optional · competitor URLs. Up to three competitor pages on the same query set, for relative scoring.

Outputs

Output 01

Per-dimension scores (0–100). Schema.org coverage · AEO snippet ownership · LLM citation surface · internal linking · answer-engine readiness · freshness. Six numbers + the rule applied.

Output 02

Composite score + tier. 0–100 weighted average. Tier-A (75+) cite-ready · Tier-B (55–74) needs targeted lift · Tier-C (40–54) restructure · Tier-D (<40) rebuild.

Output 03

Prioritized fix-list. 6–14 specific changes, ordered by score-lift / effort. Each entry names the dimension, the change, and the projected score lift.

Definition

What does an AI SEO audit actually measure?

Traditional SEO audits (Ahrefs, SEMrush, Screaming Frog) score a page on ranking position and link profile. An AI SEO audit scores it on six AI-native dimensions: Schema.org coverage, AEO snippet ownership, LLM citation surface, internal linking, answer-engine readiness, and freshness + author signals. The output is a composite score and a per-dimension fix-list. The audit assumes you already rank — it asks whether the LLMs will cite you when the buyer asks.

Scenarios

Four sample audits across page types.

Each card is a real audit cell from the FORKOFF managed-engagement ledger. Run the live tool above on your own URL to score it.

Scenario 01

Tier-C

AI agency service page · pre-audit

Established service-page ranking #6 on Google for primary commercial query. No FAQPage schema, no Author schema, 0 LLM citations.

Composite

SchemaMixed
SnippetFail
CitationsFail

Recommendation

Add FAQPage + Author schema (8 + 4 points). Publish 12-question FAQ block (+8). Earn 3 inbound citations from cited-by-LLM domains (+10). Target Tier-B in 30 days.

Scenario 02

Tier-B

Web3 protocol comparison page

Comparison page ranking top-3 on commercial query. Solid schema, weak answer-engine structure. Cited by Perplexity but not by ChatGPT.

Composite

SchemaPass
SnippetMixed
CitationsMixed

Recommendation

Strengthen answer-engine surface (+9 from FAQ + HowTo). Add primary-source citations (+5). Push for ChatGPT cite by earning 2 inbound links from chat-cited domains (Stack, Reddit, Wikipedia adjacency).

Scenario 03

Tier-D

Pre-launch SaaS landing page

New page, no rankings yet, full schema implementation, weak internal linking + freshness signals. Zero LLM exposure.

Composite

SchemaPass
SnippetFail
CitationsFail

Recommendation

Tier-D = rebuild before re-audit. Add a topic cluster around the page (5+ spokes), publish weekly, earn 3+ inbound links. Re-audit at T+45.

Scenario 04

Tier-A

FORKOFF service-page baseline

/services/ai-search-optimization · full schema · FAQ + HowTo · 3 LLM citations on commercial query · Author + last-updated signals.

Composite

SchemaPass
SnippetPass
CitationsPass

Recommendation

Defend. Re-audit quarterly. Push citations dimension above 90 by earning a Wikipedia adjacency or a top-3 Perplexity source slot for the head query.

Comparison

Why a 6-dimension AI-native audit beats a rank + crawl audit.

Ahrefs / SEMrush / Screaming Frog measure rank + crawl + tags. AI SEO Audit measures whether ChatGPT cites the page on its target queries: the surface those tools don't see.

Feature	FORKOFF AI SEO Audit6-dimension AI-native audit + LLM citation measurement	Ahrefs / SEMrushRank + link profile + Core Web Vitals	Surfer / ClearscopeContent optimization scoring
Measures LLM citation surface across 5 engines
Audits Schema.org @graph completeness for AI search		partial
Scores AEO snippet ownership on commercial queries			partial
Returns prioritized fix-list ranked by score-lift / effort		partial
Free, no signup, no domain claim
Best for ranking + backlink monitoring on existing pages	partial
Best for surface-level on-page tag audit	partial

Fit map

Who the audit is built for.

If your decision lands in the left column, the audit is the right surface. If it lands in the right, use a different tool.

Built for this

·Marketing teams whose top commercial queries already rank #1–10 on Google but who don't show up in ChatGPT / Perplexity answers.
·Founders preparing for a pSEO + AEO build and need a baseline composite per page before scaling.
·Agencies running AI search optimization engagements that need a per-page audit artifact for the kickoff.
·Brands shifting marketing budget from rank-only SEO to LLM-cited SEO and need a measurement framework.
·AI-native SaaS that has to be cited by AI engines because its buyer is asking AI engines.

Not the right fit

·Pages with critical indexing or technical-SEO bugs. Fix the rank baseline first; audit AEO surface second.
·Branded / navigational queries (people typing your name). The audit measures commercial AI-search surface, not brand recall.
·Sites under 50 indexable pages. The cluster + internal linking dimension assumes a topic-cluster architecture exists.
·Replacing a full SEO audit. The audit assumes rank fundamentals are healthy; pair with Ahrefs / SEMrush for crawl coverage.
·Real-time monitoring. AI search surface re-weights on a 4–8-week cycle; daily re-audits are noise.

FAQ

AI SEO Audit. Questions answered.

An AI SEO audit scores a page on its LLM-citation surface — whether ChatGPT, Perplexity, Claude, Gemini, and Grok cite the page on its target queries — across six AI-native dimensions (Schema.org coverage, AEO snippet ownership, LLM citation surface, internal linking, answer-engine readiness, freshness + author). A regular SEO audit (Ahrefs, SEMrush, Screaming Frog) scores rank position + link profile + crawl health. The two are complementary; a Tier-A AI SEO score on a Tier-C ranking page is rare and worth investigating, and vice versa.

Calibration corpus: 200+ pages audited inside FORKOFF's AI-search-optimization engagement, scored against actual LLM citation outcomes at T+30, T+60, T+90. AEO snippet ownership (22%) and LLM citation surface (20%) are the highest-weight dimensions because they correlate strongest with citation lift. Schema (18%) sets the baseline. Linking (12%) is the lowest because well-cited pages survive on weaker internal linking.

Next read

Other toolsin the FORKOFF stack.

01 / 04Open

AI Search Optimization service

The managed engagement that takes the audit fix-list and runs it across a portfolio with weekly reporting + closed-loop citation tracking.

02 / 04Open

AEO Checker

Brand-level 5-LLM scorecard. Where AI SEO Audit is per-page, AEO Checker is portfolio-wide citation share.

03 / 04Open

Qualified-View Auditor

Per-post engagement quality audit. Same audit-ledger philosophy, applied to a single piece instead of a page.

04 / 04Open

Marketing ROI Calculator

5-channel deterministic ROI model. After auditing AI SEO surface, model the channel mix on the same pipeline goal.

The brand line

Stop optimizing for a search engine that's losing the buyer.

Per-page AI-SEO audit · 6-dimension composite · prioritized fix-list · re-audit at T+30 / T+60 / T+90.

Run an AI Search Optimization engagement

Talk to a strategist

Run the audit

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