AVE Studio · AI Visibility Engineering
A2 · Stage 1 — Measure: targeting based on measurement
Selector · GEO target questions chosen by measurement
Being found, included and cited when an AI generates an answer — at answer time, from the live web.
Generative Engine Optimization: the definitionYou may rank well on questions your buyers rarely ask. We assemble the approved target prompt set that the rest of the GEO chain and the fixes are built on.
The machine-equity loop — Selector
- 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 definitionAI visibility shouldn't be improved in general — it should be improved on the specific questions your buyers actually ask and that can turn into business. Many companies either optimize for invented questions or try to target everything at once. Both waste money.
The Selector first organizes your services into a catalog you approve. It enriches this with Google Search Console data and industry keyword research, then assembles candidate target questions. Measurement decides which questions deserve your focus.
Service catalog
A clear, structured map of your services. You approve it before any analysis builds on it.
Pre-screening
Before any paid measurement, we filter out questions that don't fit your ideal customer profile, your target market or your subject.
Measurement on six AI engines
We check whether the AI names specific providers in its answer, and whether you are among them.
Final approval
The measurement produces a final target list of 15–25 questions. It becomes the basis of further measurement only after your approval.
Every service runs on an application we built in-house for exactly this job — not manual spreadsheets, not general-purpose tools.
Our prompt-portfolio engine: catalog-based pre-screening, per-buyer-type phrasing measurement, and a cost gate before every paid run.
- Target questions chosen by measurement: 15–25 questions in two groups: more direct revenue opportunities and positions you can win faster.
- Executive summary: a shareable HTML and PDF report built to support decisions, not just for the archive.
- AI visibility gap list: shows on which commercially valuable questions the AI recommends other providers — and exactly whom.
- Transparent costs: we give an estimate before every paid measurement, and an itemized breakdown of actual costs afterwards.
Reports are delivered in Hungarian and English.
The AI reveals your real competitors
We don't verify a pre-assembled competitor list. The measurement shows who the AI recommends instead of you — including players you didn't know about.
Questions not selected now are not lost
Commercially valuable questions that are currently hard to win go into the report as content opportunities, together with the sources cited instead of you.
You make the final call
You approve both the service catalog and the final question list. The system proposes and argues with measurement data, but the business decision is yours.
You never pay twice for the same measurement
Earlier results are stored and reused. Before any costly measurement, we run a cheaper pre-screen.
Measurement per buyer type
We can measure the same question phrased the way different buyer types ask it, because a CEO and a procurement manager don't ask alike.
Business value and realistic naming odds
We score questions on two axes: how much business value they carry, and how realistic it is — by measurement — that the AI names you. We don't start from estimated search volume.
How much does the buyer-question portfolio cost?
From HUF 140,000 net (approx. €350), with one full measurement run included. It is our most compute-intensive module because the questions are not invented — they are validated by measurement: only prompts where AI answers actually decide a recommendation make the portfolio.
What do I receive?
A real buyer-question portfolio across five intent categories, validated by measurement, with a nameability gate. This becomes the yardstick: your visibility is tracked on these questions, and the later modules (gap diagnosis, fixes) build on this exact set.
How is this different from keyword research?
Keyword research follows Google's search logic, and with most SaaS tools you end up writing the prompts yourself. We measure what buyers actually ask an AI — full questions, not keywords — and only measurement-worthy questions stay in the set.
Why pick the questions now? Every month spent optimizing for the wrong questions costs twice: once in the work, once in the mentions you did not get. Choosing target questions is a one-off step — after it, every measurement and every fix is comparable on the same list.