Compare brand mentions in model answers, then turn your findings into a costed plan and an execution tracker. The hosted default is OpenAI GPT-4o mini. Prepared examples are free and illustrative; ordinary model requests use your allowance. Projections show their assumptions and are never guarantees.
See the four actsFree · No signup · No credit card. Instant results run on a clearly-labeled sample panel, so you can judge the full report before telling us anything — not even your email.
AI visibility is how often AI assistants — Claude, Perplexity's answer engine, and their peers — mention, cite, or recommend your brand when buyers ask real questions. It is measured with three metrics: AI share of voice, citation share, and sentiment.
A growing share of buying research now ends inside an AI answer instead of a page of blue links. When a buyer asks an assistant "what's the best tool for X," the assistant names three or four brands — and everyone else is invisible. There is no page two. There is no impression report. If you are not measuring the answers directly, you do not know whether you exist in them.
Here is the uncomfortable part: your Google rankings do not transfer. Independent studies from Ahrefs and Grow&Convert found near-zero correlation between a page's Google position and whether AI assistants cite it. Ranking #1 and being cited by AI are separate problems with separate playbooks — which is why AI search optimization is now its own discipline, not a line item inside your SEO retainer.
Correlation between Google rank and AI citations. Ranking first doesn't make you the answer.
(Source: Ahrefs; Grow&Convert)
Web mentions correlate with AI visibility roughly three times more strongly than backlinks, across a 75,000-brand study.
(Source: Ahrefs)
The discipline you know: make pages that rank in blue links. Still essential — but Google position and AI citation are separate problems, and winning one doesn't win the other.
Structure content so answer engines can lift it verbatim — featured snippets, AI Overviews, answer boxes. Necessary plumbing, but visibility without attribution is a half-win.
Become the brand generative AI systems trust, recommend, and cite when your buyers ask. This is what GetCited measures and plans for.
You'll also hear "LLM SEO," "LLM optimization," and "AI SEO" — in practice, near-synonyms for GEO: the work of earning visibility in AI-generated answers. And one disambiguation worth stating plainly: GEO here means generative engine optimization, not geographic SEO — a different problem entirely. New to all of this? See the GEO questions we answer most →
You get a score. "Your brand appears in 18% of AI answers." Interesting.
You get a screenshot. It goes in Thursday's deck. Everyone nods.
Nothing ships. Next month, the same score. The dashboard became an expensive report — what one buyer, quoted in Digiday, called "just a benchmarker."
GetCited was built backwards from the plan. The four acts below exist to make one document real: a costed, owned, dated plan to get your brand cited — and a tracker that holds it accountable.
GetCited asks the questions your buyers actually ask — using the configured OpenAI model by default, with other supported providers available on your keys. The panel records who gets mentioned, who gets cited as a linked source, and how each brand is described. And here's the honest part: one panel run is a snapshot, not a statistic — we say so on the report instead of pretending otherwise. What comes back is your AI share of voice: benchmarked against competitors, aggregated across the panel, labeled for exactly what it is.
Mention share vs citation share. Being named is not being sourced. We report both, separately — because "the model knows you exist" and "the model sends buyers your way" are different problems.
Sentiment, across the panel. It's not just whether you appear — it's whether you're "the affordable option" or "the category leader." We track how AI describes you.
Buyer-intent queries, not vanity prompts. The panel runs the commercial questions — "best X for Y," "X vs Z," "is X worth it" — where citations decide revenue.
Directional by design. A single-run panel is an observation, not a distribution. We report it as directional — never dressed up as a statistic — so you know exactly how much weight it can bear.
24 buyer-intent queries × 2 engines · single-run snapshot, reported as directional · methodology below
In this sample, 3 listicle pages drive most of the category's citations — and none of them mention you. That's the gap the plan will attack first.
Knowing your score is 18% tells you nothing about why. So when the panel sees an engine cite a source, GetCited fetches that page — respecting robots.txt and rate limits — reads it, and categorizes it: comparison listicle, documentation, third-party review, community thread, original research. Then it maps which citation categories your competitors own and you don't. Every gap in your diagnosis links to a real page a real engine actually cited, not a black-box relevance score. You can click the evidence. That's the standard diagnosis should meet.
Evidence, not vibes. Each finding cites the crawled page behind it — URL, category, and how many answers it appeared in.
Category gap analysis. "You're absent from comparison listicles" is actionable. "Your score is low" is not.
Competitor presence mapping. See exactly which cited pages name your competitors and skip you — those are your highest-leverage targets.
Respectful crawling. robots.txt honored, rate-limited, no scraping arms race. The diagnosis is built to be defensible.
This is the part every other tool skips. Give GetCited three constraints — budget, who's available, and when you need movement — and its allocator selects the tactics that close your biggest citation gaps per dollar, in order, until the budget is spent. Not a generic checklist: a committed roadmap where every action ships as WHAT, WHY, HOW, and WHO, scheduled to a week, grounded in the crawled evidence from Act 2. Export it and walk into Monday's meeting with the document this category has been refusing to write.
Costed, not aspirational. Every tactic carries an effort estimate, and the plan totals against your actual budget — you see what's in, what's out, and why.
WHAT / WHY / HOW / WHO / when. Each row is assignable on sight. The WHY always cites the diagnosis evidence behind it.
A projection that shows its work. The plan states its modeled share-of-voice lift with its full assumption list and a confidence rating — low, medium, or high — attached. It is an estimate. It says so, on the document.
One-click export. DOCX for your boss, PDF for the deck, Excel for the ops tracker — because plans that live only inside a SaaS tab don't get executed.
"First reply received — sending comparison one-pager."
4 of 9 items on track, 1 at risk: the listicle pitch is due in 3 days and still awaiting a reply. Want me to draft the follow-up email, or re-run the benchmark to check for early movement? (Re-benchmarking is a paid run — I'll only do it if you say so.)
Say yes to the plan and GetCited turns it into a living tracker — one editable execution item per action, due dates derived from the schedule you approved. Update statuses, drop remarks, reassign. Then close the loop the way this category never does: ask, in plain language, "how are we progressing?" — and get an answer computed from your tracker's actual state, not from a fresh benchmark. Re-running the panel costs real money, so GetCited only re-benchmarks when you explicitly ask it to. Your tracker is the source of truth between measurements. That's the difference between a monitoring tool and an operating system for getting cited.
One item per action, editable inline. Status, remarks, dates — it's your tracker, not a read-only report.
Progress answers from your data. "Are we on schedule?" is answered from item statuses and due dates — instant, free, honest.
Re-benchmark on request only. Measurement costs money; we never burn your budget silently to decorate a chart.
The loop actually closes. Measure → Diagnose → Plan → Track → measure again — each cycle starts from evidence of what you actually shipped.
Every other tool sells you the mirror. GetCited sells the mirror, the itemized repair bill, and the repair tracking.
GetCited is agent-first — a chat drives the entire pipeline, so "learning the tool" mostly means asking questions in plain English. And with the GetCited MCP connector, you may never open our UI at all. Add it to Claude once, and everything you just scrolled through becomes a conversation: benchmark your brand, pull the report, build a plan against a budget, approve it into the tracker, check progress, draft the content the plan calls for. This is not a read-only data feed bolted onto a chat window. It's nine tools covering the full Measure → Diagnose → Plan → Track loop, so your AI visibility program lives where you already work.
Works with any MCP-capable client. You authorize with your own GetCited login — no pasted API keys in chat.
You will find vendors in this category promising multiplied share of voice in 60 days. Be suspicious: these systems answer differently every single run — anyone quoting a guaranteed lift from a non-deterministic system is selling you the variance. The sharpest criticism of AI visibility tools is that they hide their methods — undisclosed prompt lists, unknown run counts, opaque scoring — and then headline guaranteed lifts. GetCited was built as the rebuttal. Every projection is a modeled estimate with its assumptions stated and a confidence rating attached. Every panel run is labeled for what it is — a single-run snapshot, reported as directional. And the methodology behind your numbers is disclosed, not hidden — because a measurement you can't interrogate is a measurement you can't trust.
"Guaranteed 6× share of voice in 60 days."
"This plan models a 4–9 point share-of-voice lift over ten weeks, assuming the three comparison-page placements land and your docs become crawlable. Here's the confidence rating. Here's the tracker."
The second sentence is example phrasing — your plan's numbers come from your panel, your gaps, and your budget.
Prepared examples are illustrative and free. Ordinary probes record an actual configured-model response; a single response is directional, not market research.
Tactics selected against your actual budget, each with WHAT/WHY/HOW/WHO and a date. The projection states its assumptions on the exported document itself.
The tracker answers from your own execution data. When we re-measure, you see movement against the same disclosed methodology — run to run, not screenshot to screenshot.
A GEO tool whose own homepage can't get cited would be a bad joke. So this page is generative engine optimization, practiced on ourselves:
Ask your favorite assistant what AI share of voice is. If it answers in our words, you'll know this section worked.
Every AI visibility tool burns LLM calls on your behalf. Almost none of them tell you whose keys, where your data goes, or what's retained. We think that's a question a measurement company should answer on its homepage — so here's ours, in full.
Zero setup. Our keys, our infrastructure.
Bring your own keys. They stay yours.
Same product, same methodology, either mode — custody is a setting, not a plan tier. Your workspace data is row-level-secured to your account, and everything the plan produces exports with one click.
These answers are written to be quoted — by you, by your boss, or by an AI assistant. They ship as FAQPage structured data, exactly as you read them here.
AI visibility is how often AI assistants — Claude, Perplexity's answer engine, and their peers — mention, cite, or recommend your brand when buyers ask real questions in your category. It's measured by running those buyer-intent questions across a panel of engines and recording three things: share of voice (how often you appear vs competitors), citation share (how often your pages are linked as sources), and sentiment (how you're described). GetCited provides labeled prepared examples and ordinary configured-model runs — and because one panel run is a snapshot, not a statistic, the report says so plainly instead of dressing the numbers up.
Generative engine optimization (GEO) is the practice of improving how generative AI systems represent, recommend, and cite your brand in their answers. Where SEO earns rankings in blue links, GEO earns mentions and citations inside AI-generated answers — a different surface with different rules. Answer engine optimization (AEO) and LLM SEO are near-synonyms; whatever you call it, the success metric is citations in AI answers, and that's the number GetCited measures and plans against.
SEO gets your pages ranked in search results; AEO gets your content lifted as the direct answer in snippets and AI Overviews; GEO gets your brand trusted and cited by generative AI systems like Claude and Perplexity. The tactics overlap — structured content, third-party validation, entity consistency — but the success metrics differ: clicks for SEO, answer placements for AEO, citations for GEO. Winning one does not automatically win the others, which is why AI answers have to be measured directly.
There's no pay-to-play: AI assistants mention brands based on training-data associations and live retrieval of pages they trust. In practice the drivers are presence on third-party 'best of' lists and review sites, original research worth citing, well-structured extractable content, and consistent brand information across the web. GetCited's Diagnose step crawls the pages AI engines actually cite in your category and shows exactly which of those pages skip you — then the Plan step turns each gap into a costed, assigned tactic.
Two mechanisms: brand associations learned in training data, and live retrieval of crawlable, well-structured pages that reflect web consensus. Notably, Ahrefs' study of 75,000 brands found web mentions correlate with AI visibility far more strongly than backlinks (0.664 vs 0.218) — the currency of AI citation is being talked about on pages engines trust. The mix also differs by engine, which is why GetCited runs a multi-engine panel instead of extrapolating from one.
No. Independent studies by Ahrefs and Grow&Convert both found near-zero correlation between Google rankings and citations in AI answers — the two systems select sources in fundamentally different ways. You can dominate page one and be invisible in AI answers, which is why treating AI visibility as a side effect of SEO is the most common measurement mistake in this category. You have to measure AI answers directly, which is exactly what GetCited's panel does.
Run the same buyer-intent questions across each engine, record every mention, citation, and sentiment signal, and benchmark the results against your competitors over time. Doing this manually is possible but slow. GetCited automates it as a multi-LLM panel — OpenAI by default, with other supported providers available on configured keys — and reports AI share of voice and citation share aggregated across the panel. One run is a snapshot, not a statistic, and the report says so instead of pretending otherwise.
GetCited computes AI share of voice as a brand’s answer mentions divided by total mentions across the tracked brands. GetCited splits it into two metrics because they answer different questions: mention share (how often you're named at all) and citation share (how often your pages are linked as sources — the stronger signal, since it sends buyers to you). Both are aggregated across the panel and benchmarked against the competitors you configure.
Honest ranges from published practitioner data: technical and crawlability fixes can register in about two weeks, first new citations typically appear in four to six weeks, and durable share-of-voice movement usually takes two to three months — varying by engine refresh cycles and how contested your category is. Anyone promising faster guaranteed results is ignoring how these systems actually update. GetCited's plan attaches a timeline to every tactic, and the Tracker answers 'are we on schedule?' from your actual execution status.
Large language models are non-deterministic: the same question can produce different answers depending on the run, phrasing, geography, and model version. That's why a screenshot of one good answer is an anecdote, not a measurement — and why your manual spot-check rarely matches a tool's report. GetCited's panels are single-run today, and the report labels them exactly that way — a directional snapshot, never dressed up as a statistic — so you always know precisely how much weight the numbers can bear.
No — and you should be suspicious of any vendor who says yes, because these systems answer differently on every run and no one controls their outputs. Every projection GetCited produces is a modeled estimate with its assumptions stated and a confidence rating — low, medium, or high — attached. What we do commit to is verifiable: ordinary measurements record actual configured-model responses, the plan is costed against your actual budget and team, and the tracker shows progress you can audit line by line.
You choose between two modes. We Serve runs the panel on GetCited's own keys and infrastructure — zero setup, transparently metered. Self Serve lets you bring your own provider keys, which are held in this tab’s session storage by default and cleared on sign out or account change (browsers may restore tab storage); if you prefer convenience, you can opt in to AES-GCM encrypted storage and remove your keys at any time. Key values are never logged and never displayed back — the app only ever checks that a key is present.
The audit is free, instant, and asks for nothing — not even an email. If the report isn't useful, close the tab; you've lost a minute. If it is, you'll have your gap list — and we'll have earned the next conversation.
The instant audit runs on clearly-labeled sample data — it exists so you can judge the product before trusting it with a real run. We'd rather show you an honest demo than a rigged live number. Real panels start when you connect a brand — on our keys, or yours.