Per our own AI-Overview audit of 1,026 buyer queries (June 2026), 879 returned an AI Overview and 233 of those cited FORKOFF.
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.
The rerun is the part that proves the sprint worked, not just started. Our 50-prompt citation lab rerun across the same 5 AI surfaces moved the average cite rate from 22% to 34% between the February baseline and the May 2026 rerun. That is the same probe-score-recommend loop this checker runs, just on a fixed cadence instead of a single pass.
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.
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 plus a 90-day GEO sprint recommendation.
It probes a brand against ChatGPT, Perplexity, and Claude on 5 high-intent commercial queries (e.g. 'best AI marketing agency', 'top GEO agency 2026'). For each query × LLM pair, it records whether the brand was cited, in what position, what was said about it, and the sentiment of the mention. The tool then aggregates the results into a visibility score (0-100), a grade band, and a 90-day GEO sprint recommendation tied to the queries where the brand is absent.
80% of buyers now ask an LLM before they Google. If your brand does not surface in the cited source set when ChatGPT, Claude, or Perplexity answer your category's commercial queries, you are functionally invisible to the buyer's first research pass. SEO traffic to your owned site does not necessarily translate; LLMs ground their answers on a different citation graph (directories, listicles, comparison pages, parasite SEO, structured Q&A). Tracking this surface is the new search console.
No. A Google SEO audit looks at on-page signals, backlinks, Core Web Vitals, and how Googlebot ranks pages. The AI visibility checker looks at whether the brand is cited inside the natural-language responses LLMs generate, which is a different end surface. The two surfaces overlap (good schema and authoritative content help both) but a brand can rank #1 on Google and still be invisible in ChatGPT, and vice versa. Run both audits, then compare the gap.
v1 ships a static sample report so the pillar surface, education, and methodology are public. The live ChatGPT + Perplexity + Claude API pass is in private beta; access is gated to applicants with a real audit project to anchor the calls against. This avoids burning API budget on idle tire-kickers and keeps the tool calibrated against the FORKOFF qualified-view bench. Apply for access via the CTA below.
Five high-intent commercial queries that buyers in your category actually run. Mix three head-term queries (e.g. 'best web3 marketing agency') with two long-tail intent queries (e.g. 'fractional CMO for pre-Series-B AI startup'). The visibility distribution across head vs long-tail is more informative than either alone; head-term hits prove brand recall, long-tail hits prove specific-intent fit.
Low visibility (under 50) means the citation graph LLMs retrieve from does not yet recognize the brand as a credible answer for the queries you care about. The fix is engineering the corpus those models retrieve: directory listings (G2, Capterra, Clutch), parasite SEO ladder (Medium, dev.to, HackerNoon), comparison pages, listicle inclusion, schema validity, and authoritative third-party citations. FORKOFF runs this as a 90-day Generative Engine Optimization sprint, outcome-priced and tracked against weekly citation lift on the same query set.
Both. The methodology is identical; only the query set differs. AI startups probe queries like 'best AI marketing agency', 'top AI tool for X', 'AI SDR alternatives'. Web3 protocols probe 'best web3 marketing agency', 'top crypto launch agency', 'KOL marketing for token launch'. The audit ledger that backs the tool is calibrated against 200+ FORKOFF campaigns split across both lanes.
An LLM visibility checker measures whether large language models name your brand when they answer questions in your category. It is the same idea as an AI search visibility checker: the FORKOFF tool probes ChatGPT, Claude, Gemini, Perplexity, and Google AI Overview, records where and how your brand is cited, and returns a 0-100 score plus the queries where you are absent. The term LLM visibility checker just names the surface after the models instead of after the search experience.
In practice, yes. LLM SEO checker, LLM visibility checker, and AI search visibility checker all point at the same job: measuring whether AI answer engines cite your brand and giving you a way to improve it. The naming varies because the category is new. What matters is the method behind the score, not the label. This tool measures citation presence, position, and sentiment across five AI surfaces, then maps the gaps to a 90-day Generative Engine Optimization plan.
The best AI search visibility checker clears six bars: it probes every major engine rather than one model, scores citation position and not just presence, reads sentiment rather than counting mentions blind, covers both head and long-tail queries, benchmarks the score against real campaign data, and returns a concrete fix path instead of a bare grade. A tool that stops at a single number is a vanity metric. Judge any checker, including this one, against those six criteria.
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.
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