Skip to content

AVE Studio · AI Visibility Engineering

End-to-end GEO: AVE Studio builds and implements the fix

AVE Studio builds the fix and implements it — in code, in content, in domain architecture and in the link graph. Which fix is worth building is decided by the first three steps of the chain: measurement, target prompt set, gap analysis. The round closes with the same method it opened with: a re-measurement.

Request the AssessmentAssessment · a fixed fee, credited to the work

Updated ·

Three steps before end-to-end GEO builds anything

The diagnosis is not the product — its job is to stop us building the wrong thing. These three steps decide what is worth spending engineering on.

  1. Measurement — does AI name you today, when your buyers ask. Per engine and per language, with repeated queries and confidence ranges. This is the baseline the end of the round measures back against.
  2. Target prompt set — which real buyer questions are worth competing for. Not the ones that are easy to win, but the ones your buyers actually ask. You approve the set; nothing ships without that approval.
  3. Gap analysis — question by question, why the engine picks someone else. What is missing: the claim, the structure, the source, or simply the engine never reaching your page at all.

Five streams the end-to-end GEO fix arrives in

From here you do not get advice, you get finished work. All five streams land on your own surface, in reviewable form.

  1. Code — the fix in your codebase: server-rendered content, a JSON-LD entity graph, crawlable markup. It arrives as reviewable changes, not as a list of recommendations.
  2. Content — we write and restructure the pages an answer engine has to be able to quote: the claim the buyer question demands, at the chunk boundary retrieval actually cuts on.
  3. Domain architecture — slug, title, H1 and the H2–H4 heading structure. This is the layer a machine reads first: it makes a page's subject unambiguous and keeps its sections intact through chunking. Redirect discipline belongs here too: no internal link should ever point at a 301.
  4. Internal links — rebuilding the link graph so the pages that answer buyer questions are reachable, never orphaned, and linked with anchor text that states what they are about.
  5. External links and link hygiene — auditing and correcting the outbound and inbound link surface: dead and redirected targets, inconsistent entity references, citation-worthy off-site presence.

Closing the round: re-measurement by the same method

The closing measurement runs on the same target prompt set, the same engines and the same procedure as the baseline. That is the only way the two numbers compare — an "improvement" measured on a different panel is not a result, it is a different measurement.

The raw answers are yours: every query output stored unchanged and dated. If the number did not move, we show you that too — a measurement is only a measurement if it can bring bad news.

Why AVE Studio?

AVE Studio works to get your brand named and cited by AI answer engines. We do not advise on it — we deliver: entity architecture, machine readability, the access layer and answer-ready content, in code, into your system. That is the GEO, AEO and KEO work.

We start with measurement, because without it nobody knows what worked — but measurement is the instrument, not the goal. We do not guarantee placement, because in a generated answer nobody can. What we do commit to: a baseline measurement, a checkable intervention in code, a re-measurement by the same method, and the raw data stays yours. Our methodological requirements are fixed in a public, 18-point standard.

The 18-point methodology standardPublic measurements

Frequently asked questions about end-to-end GEO

The four questions we are asked most often about the chain.

Can we stop after the audit?

Yes. The baseline audit is a standalone product: you get the measured state, the confidence ranges and the raw data, and you decide from there — including building the fix with your own team. The chain is a sequence, not a bundle.

Why do I have to approve the target prompt set?

Because the set decides what we measure and what we build. Compete on the wrong questions and the number can move while revenue does not. The approval is the point where your market knowledge overrides our sampling.

Do you work in my own codebase?

Yes, that is the default: the fix arrives as reviewable changes where your site actually lives, and goes live through your process. Where that is not possible, the delivery comes as ready-to-insert code and copy.

How long before anything shows?

Measurement takes days; the effect of a fix takes weeks. We do not commit to a date for appearing in answers, because that depends on the engines' indexing and recall cadence, not on us. What we do put a date on is the delivery and the re-measurement.

Where to go from here

If you would rather look at the service as a whole first, or buy module by module, these two pages are the way in.

Why start a round now? The first and last step of the chain are the same measurement — and at the end we can only compare the result to what we recorded at the start. Every month without a baseline is a month there will be nothing to say about later.

Let's start — step one is the measurement

Write or call, and we will tell you what AI says about your company today — from there the question is which fix is worth building.

hello@avestudio.pro+36 30 900 1356

The journey

One studio, the whole journey.

From first diagnosis to buying inside AI — one continuous road, not scattered services.

Along the way we track three measures —
Naming rate

How often AI names you when your buyers ask.

Naming rate: the definition
,
Model Mindshare

The share of an AI model's recall a brand holds in its category — a measured signal with a confidence range, not a guarantee. Measured closed-book (with web search off) as a training-time, parametric signal — the generation after retrieval-time GEO visibility.

Model Mindshare: the definition
,
Machine readability

How much of your site an AI crawler can actually read.

Machine readability: the definition
— always reported separately, with ranges, never merged into one score.

The journey in three tiers: Tier 1 Be seen (live), Tier 2 Be known (live), Tier 3 Be bought (roadmap).

Tier 1 · Signal — Be seen — GEO/AEO — Live

01 · Citation Tracker

The baseline: how often AI names you — per engine, with a confidence range.

Citation Tracker — more about the service
02 · Selector

Picks the 15–25 buyer questions worth competing for — by measurement, not guesswork.

Selector — more about the service
03 · Gap Analysis

Question by question, why the AI recommends someone else — with evidence.

Gap Analysis — more about the service
04 · Gap Closure

Paste-ready fixes: code, copy and llms.txt, fitted to your site.

Gap Closure — more about the service

Tier 2 · Memory — Be known — KEO — Live

KEO modules · all four modules live

K1 · Model Memory Audit

Closed-book measurement: what the model remembers about you — web off, per engine and per language, with confidence intervals.

Model Memory Audit — more about the service
K2 · Entity Anchor

One clean, verifiable entity: Wikidata, a consistent sameAs web, a canonical fact set — and source-level fixes for false “memories”.

Entity Anchor — more about the service
K3 · Corpus Campaign

Presence where models actually learn: licensed press, earned community surfaces, a content calendar timed to training cutoffs.

Corpus Campaign — more about the service
K4 · Release Audit

At every major model release we re-measure what the new generation remembers — the delta is the only honest proof.

Release Audit — more about the service

Tier 3 · Market — Be bought — B2M — Roadmap · in build