AVE Studio · AI Visibility Engineering
A1 · Stage 1 — Measure: the baseline everything builds on
Citation Tracker · GEO AI visibility measurement
Being found, included and cited when an AI generates an answer — at answer time, from the live web.
Generative Engine Optimization: the definitionWhen your buyers ask AI for a recommendation in your category, does it name you? We measure the AI visibility baseline on each surface separately, and record the baseline every later fix is measured against.
The machine-equity loop — Citation Tracker
- 1 · MEASURE — “Where do I stand today?” Per engine, with a range (Citation Tracker, Selector)
- 2 · FIX — “Why do they skip me?” Page by page, then fixed in code (Gap Analysis, Gap Closure)
- 3 · ANCHOR — “Where does the machine learn about me?” An unambiguous entity, present where models learn (Entity Anchor, Corpus Campaign)
- 4 · PROVE ↺ — “Did it move, and what did it bring?” Closed-book baseline and the delta at every new model generation (Model Memory Audit, Release Audit)
KG Doctor and the Claim Coherence Engine — the studio's own instruments — serve the entire loop.
Machine equity is the business asset that arises from AI systems being able to recognize, understand, recall, recommend — and, on a user's behalf, transact with — a brand. Composed of three layers: rented reach (retrieval visibility), owned memory (trained model recall), and machine buyability (agent-side transactability). Measured honestly only as a portfolio of ranged, per-layer metrics — never as a single blended score.
Machine equity: the definitionYour buyers increasingly ask AI assistants for answers. These surfaces don't list ten links — they typically name just a few brands or providers. The Citation Tracker measures how often you are one of them. We ask real buyer questions on the six AI engines we measure — ChatGPT, Gemini, Claude, Perplexity, Google AI Overview and Google AI Mode — and record who the AI mentions, recommends or cites.
AI doesn't always give the same answer to the same question. So by default we ask every question ten times. We report the result not as a deceptively precise number but as a confidence range that shows how stable the measurement is.
Brand name, website address, industry and a short description of your target market, in one questionnaire. Optionally: your own test questions, server access logs or GA4 data.
Quick audit: we examine ten buyer questions on two AI engines, and give you a visibility score from 0 to 100. A no-obligation picture of where you stand today.
The entry quick check is the only service we offer that returns a single combined score — for orientation. Every paid measurement reports ranges, broken out per dimension.
Buyer questions
We compile the questions that actually come up in your market, based on your client profile, then add your own questions.
Measurement on six AI engines
We ask every question ten times on five engines: ChatGPT, Gemini, Claude, Perplexity and Google AI Mode. We also measure the sixth surface, Google AI Overview, but repetition there is different in kind, so it stays out of the repeat count.
AI-assisted evaluation
The system doesn't just look for your brand name. It recognizes reworded mentions, indirect recommendations and the tone of the answer.
Dashboard and report
Results appear per engine, with trends over time. The online report is shareable, can be password-protected on request, and is available as a PDF.
Every service runs on an application we built in-house for exactly this job — not manual spreadsheets, not general-purpose tools.
Our measurement system: repeated queries across six AI engines, AI-assisted evaluation, confidence ranges — with a dashboard and shareable reports.
- Naming rate per AI engine: we don't blur the results into one generic "AI score".
- Competitor share: shows who the AI names instead of you, and in what order.
- The list of cited sources: shows which websites the AI draws on when it answers about your category.
- The tone of answers about you: shows whether the AI recommends you, merely mentions you, or speaks about you with reservations.
- Real AI traffic arriving on your website: measured from server logs or GA4 data, and kept separate from whether the AI mentions you.
- Change between measurement rounds: shows how your AI presence evolves from one measurement to the next.
Reports are delivered in Hungarian and English.
A separate result for every AI engine
A single merged score would hide which engine has the problem. So we show each engine's result separately.
Repeated measurement, with a confidence range
A one-off query easily returns a random result. We measure repeatedly, and we also show how stable the result is.
Not mere keyword matching
We recognize when the AI refers to you in other words, recommends you indirectly, or cites your content without naming your brand.
A mention and a click are not the same thing
We report separately how often the AI mentions you in the measurement, and how much actual traffic arrives on your website from AI surfaces.
How much does the baseline visibility audit cost?
The baseline audit starts from HUF 95,000 net (approx. €240). The base price covers the core scope: machine measurement across the major AI surfaces in one language with the core question set, your naming rate with a confidence interval, a 60-minute readout call and the written report. Extra languages or an extended question set are quoted as a fixed add-on after scoping. It is the entry step: every later module builds on this baseline.
What do I receive?
A measurement report on whether you appear in the AI answers your buyers actually see: per-question breakdown, naming rate with confidence intervals, a list of competitor mentions, and the readout call. The report is a re-runnable baseline — every later change is measured against it.
How is this different from a free AI audit?
Free audits typically judge from a single query — but AI answers vary from run to run. We measure with repeated sampling, confidence intervals and multiple surfaces, so you learn what is stable and what was chance. It is an instrument, not a checklist.
Why measure now? AI answers are where your buyer decides who to look at, before they call anyone. Until you have a measured baseline, every later fix is guesswork about whether it worked — and today's state cannot be measured retroactively. A baseline takes days; not having one costs you months of being able to prove anything.