Probe 8 commercial queries across 5 AI search surfaces (ChatGPT, Claude, Gemini, Perplexity, Google AI Overview) in one pass. Get a citation map, sentiment classification, and a 90-day Generative Engine Optimization sprint recommendation calibrated against the FORKOFF qualified-view bench. The only FORKOFF tool that actually queries LLMs - for page-level HTML signal audit use AEO Checker, for domain agent-readiness substrate use GEO Audit, for a unified composite use AI SEO Audit.
Outcome-priced · audit ledger weekly · AI + Web3 lanes both supported
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An AI visibility checker is a tool that measures whether a brand is cited in the answers AI search engines generate. The FORKOFF AI Search Visibility Checker probes your brand across ChatGPT, Claude, Gemini, Perplexity, and Google AI Overview on high-intent commercial queries, then scores each citation by position and sentiment and returns a 0-100 visibility score with a 90-day Generative Engine Optimization sprint recommendation.
Most SEO tools tell you Google's answer. Most LLM trackers stop at one model. FORKOFF probes the citation graph all three retrieve from.
Three steps tied to the same audit-proof methodology that runs every FORKOFF qualified-view campaign. Same bench across AI startups and Web3 protocols.
ChatGPT, Claude, Gemini, Perplexity, plus Google AI Overview. Eight high-intent commercial queries you actually care about. The probe captures the full natural-language response, not just an embedded citation list.
40
Query × surface pairs per check.
For each pair: was the brand cited, in what position relative to competitors, what was said about it, and is the sentiment positive, neutral, negative, or absent. Visibility score 0-100 + grade band.
0-100
Visibility score across all probes.
Output a concrete sprint: which directory listings to target, which parasite hosts to publish on, which comparison pages to ship, which schema gaps to close. Outcome-priced execution with weekly citation-lift reporting.
90 days
Default sprint window. Calibrated, not estimated.
The score is a single 0-100 read of how often ChatGPT, Claude, Perplexity, and Google AI Overview name your brand when they answer the commercial queries in your category. Most sites we probe land under 50. When we ran an agentic SEO audit across 14 properties, the median scored 9 out of 100. Here is what each band means operationally.
The retrieval graph does not recognize the brand for these queries. The models answer your category without naming you, so the buyer never sees you on the first research pass. Every head-term query is an open gap.
The brand surfaces on a few branded or navigational prompts but drops out of the head-term commercial queries that decide deals. Recall exists, intent coverage does not.
Cited on most head terms but inconsistent on long-tail intent queries, and rarely the first source named next to competitors. The base is there, the position is not.
Named across head and long-tail queries, usually inside the first two sources, with neutral to positive sentiment. This is the position that compounds, because models cite what they have cited before.
A low band is not a content problem, it is a retrieval problem. The write surface that moves the number is Generative Engine Optimization: the directory, comparison, and schema work that puts your brand into the source set these models pull from.
The first research pass for any commercial query has shifted from a search engine to a chat interface. ChatGPT, Perplexity, and Claude do not surface ten blue links. They synthesize an answer and cite a small set of sources. If the brand is not in that cited source set, the buyer never sees it.
This is a different surface from Google SEO. LLMs ground answers on a different citation graph: directories, listicles, comparison pages, parasite SEO posts, structured FAQ schema, third-party endorsements. A brand can rank position one on Google and still be functionally invisible in ChatGPT. Our teardown of how AI Overviews actually rank brands unpacks that citation graph in detail. The checker is the read tool for that surface; Generative Engine Optimization is the write tool.
forkoff.xyz dog-foods this. Self-audit 2026-05-04: 3 LLM hits across 24 commercial queries. Outcome-priced GEO sprint kicked off the same week.
Three ways to read your brand's surface. Only one ties the read to a write surface (Generative Engine Optimization) anchored on outcome-priced execution and a weekly citation-lift proof.
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| Feature | FORKOFF AI Search VisibilityCitation map · sentiment · GEO recommendation | Generic SEO audit toolBacklinks · on-page · SERP rank | Manual ChatGPT spot-checkFree · ad-hoc · no benchmark |
|---|---|---|---|
| Probes ChatGPT, Perplexity, Claude | Yes, all three | No (Google only) | Manual, one at a time |
| Citation position scoring | Yes, per-query position rank | N/A | Eye-balled |
| Sentiment classification | positive / neutral / negative / absent | No | No |
| GEO sprint recommendation | Yes, anchored on absent queries | No | No |
| Audit ledger benchmark | FORKOFF qualified-view bench | N/A | N/A |
| Pricing | Free v1 demo, audit by application | $99-499/mo SaaS | Free, your time |
| Outcome-priced execution path | Yes, weekly citation-lift ledger | No | No |
An AI search visibility checker, also called an LLM visibility checker or an LLM SEO checker, is only as useful as what it measures. A score with no method behind it is a vanity metric. When we tested seven visibility platforms against the FORKOFF methodology, the ones worth keeping cleared six bars. Use them to judge any tool, including this one.
ChatGPT, Claude, Perplexity, Gemini, and Google AI Overview each pull from a different source set. A checker that queries one model reports a fraction of your real surface.
A mention buried under three competitors is not a win. A real checker records where your brand lands in the cited order, not only that it appeared.
Being named as the example to avoid is still a citation. The tool should read what was said about you, positive, neutral, or negative, not tally mentions blind.
Head terms prove brand recall. Long-tail intent queries prove you fit a specific buyer need. A checker that runs one kind shows half the picture.
A number with no next step goes stale in a week. Look for output that names the directories, comparison pages, and schema gaps to close, the way a managed AEO program does.
A 0-100 score means nothing without a reference set. Calibrate against real campaign data across your vertical, not an arbitrary curve tuned to sell a subscription.
Sister tool. 6-dimension on-page AEO audit (schema, FAQ, definitional opener, headers, meta, agent signals). Run on the page you want cited.
Sister tool. Audits the agent-readiness substrate (robots 7-bot allowlist, llms.txt, well-known manifests, schema graph). The plumbing before LLMs can cite you.
Sister tool. Generates the structured-data that earns the citations the visibility checker is measuring. 8 schema types covered.
One audit that composes AEO + GEO + Lighthouse into a unified 0-100 scorecard. Run this after the individual audits for the consolidated view.
The 90-day Generative Engine Optimization sprint the tool maps onto. Outcome-priced, tracked and verified.
Managed AEO program. Weekly audits + the foundation work that lifts a sub-30% brand to 65% inside two quarters.
Outcome-priced execution · verified proof entry per shipped phase · BY APPLICATION · five GEO engagements per quarter across AI + Web3.
Browse all FORKOFF tools
AI Overview optimization is structural, not a domain-rating game. The 12 on-page patterns that make a page extractable and citable, with first-party data.
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How Google AI Overviews decide which brands to cite: a 4-layer selection stack, first-party citation lab data, and a 7-step optimization checklist.
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Measure your share of AI citations with a 3-metric framework, a 30-60 prompt set, per-platform scoring across ChatGPT and Perplexity, and a reporting cadence.
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