Choosing an AEO or GEO agency in Greece should start with one question: can the agency demonstrate, with repeatable evidence, how it measures whether brands are mentioned, cited and recommended by AI systems?
Published: August 2026
Last reviewed: August 2026
Reviewed by: AEO Agency, the AI Visibility practice of Bluemind
Not simply what they will do — but how they will prove that it worked. In AI Search, you cannot verify performance in the same way you can check a traditional Google ranking. The answer you see may not be the answer your customer sees, and it may not even be the same answer you saw yesterday. That makes the measurement protocol one of the most important parts of the service to evaluate before you sign.
This matters even more as the AEO/GEO market in Greece continues to expand. Two years ago, “AEO agency” was barely a category in Greece. Today, a growing number of digital and SEO agencies in Athens, Thessaloniki and across the country advertise AEO or GEO services. Traditional due diligence — “show me your rankings” — is no longer enough. You have to evaluate the methodology behind the results.
Below are ten questions to ask in the meeting, the research behind each one, and what a defensible answer sounds like.
New to the terminology? Start with what AEO and GEO actually mean, then come back to this checklist.
In short
A credible AEO/GEO agency should be able to document five things: how prompts are selected, how tests are repeated, how brand mentions, citations and recommendations are measured, how competitors are benchmarked, and how results are remeasured after implementation.
First, the ground truth about the channel
The traffic is small but unusually valuable. Conductor’s 2026 cross-industry benchmark places AI referral traffic at just over 1% of total web visits. Yet Ahrefs found AI search delivered about 0.5% of its traffic and 12.1% of its signups, and Adobe’s own reporting shows US retail visitors arriving from AI assistants converting 42% better than non-AI traffic in March 2026 — a reversal from a year earlier, when the same channel converted 38% worse. Adobe’s Q3 AI Traffic Trends report puts the May 2026 gap wider still, at 54%. (Note the scope: US retail, not Greek B2B — read it as direction of travel, not as your benchmark.) This is not a traffic-volume play. It is a pre-shortlist play: the customer arrives after the comparison, not before it.
It is no longer one engine. Similarweb’s data shows ChatGPT’s share of worldwide generative AI web traffic falling from around 76% a year earlier to roughly 53%, with Gemini past a quarter and Claude the fastest-growing platform in the category.
Being retrieved is not being cited. ChatGPT cites only around 15% of the pages it retrieves while composing an answer. The rest are read and dropped.
And the measurement itself is noisy. Kevin Indig’s analysis with AirOps, across roughly 815,000 prompt-page pairs, found that after running the same prompt three times in ChatGPT only about 2.2% of citations survived all three runs, with within-model sampling variance of 10–34% on identical prompts. Profound’s citation-drift work found that a large share of the domains cited for a given prompt are simply different a month later.
A single run is not a measurement. It is an anecdote. Everything below follows from that.
The ten questions
1. Do they use a frozen prompt set?
The prompts must be agreed at the start and held constant for the life of the engagement. An arXiv study on prompt brittleness in AI recommendations found that two natural paraphrases of the same buying intent produce recommendation sets overlapping only 14–29%, versus 50–61% for reruns of the identical prompt. The wording is itself the dominant variable. If the prompt set changes between cycles, every “improvement” you are shown may be a rewording.
Good answer: the query set is documented, versioned, and shared with you before any work begins — see how we build a frozen query set.
2. Are prompts repeated?
Because outputs are probabilistic, a prompt run once tells you almost nothing. Serious measurement re-issues each prompt multiple times — and an arXiv paper on measuring visibility in AI search re-runs prompts on the same calendar day specifically to separate model randomness from real change.
Good answer: a stated number of runs per prompt per cycle, results reported as a range or frequency, plus an explicit rule for void runs — an empty or failed response logged as such, not quietly dropped from the average.
3. Are tests performed in non-personalized sessions?
Logged-in sessions carry memory, history and account context. An agency testing from its own account is measuring its own footprint, not yours.
Good answer: logged-out, cold sessions with no personalization carried over, and a controlled location setting — which matters in Greece, where a Greek-market result and a default US result can differ completely. This is the basis of our cold-prompt measurement protocol.
4. Do they distinguish mentions from citations?
A mention means the model knows you exist. A citation means a source was linked and trusted — and a self-citation, where your own domain is the linked source, is the strongest signal of the three. An agency reporting one blended score cannot tell you why the number moved, which means it cannot tell you what to fix. We break this down in mentions vs citations vs self-citations.
5. Do they measure recommendations separately?
This is the metric closest to revenue and the one most often skipped. Being named in a paragraph is not the same as being placed on a shortlist in answer to “which agency / supplier / firm should I use?” Presence and recommendation are different questions and move for different reasons.
Good answer: recommendation prompts are a distinct block in the query set, scored separately from informational ones.
6. Do they report results per AI engine — and per language?
Given the shift in engine share above, a single aggregate number hides where you actually stand. Per-engine reporting is the minimum.
Language is the half of this question most Greek buyers never ask. An arXiv study across twelve European languages found that the assumption behind most AI-visibility monitoring — that an English query returns a representative picture — does not hold in multilingual European markets, and that the gap falls hardest on local champions rather than global brands. A separate analysis of over 7 million AI citations found local-language citation rates ranging from roughly 85% for Google AI Overviews to about 52% for Grok: a 34-point spread in how strongly each engine prefers sources in the query language. Greek was not among the languages tested in that dataset — almost no published benchmark covers Greek at all. The engine-level language sensitivity data points the same way: under non-English prompts, citations skew toward the query language, but by very different degrees per engine.
If your buyers ask in Greek, an English-only audit measures a market you do not sell to. If you export, you need both, reported apart.
7. Do they preserve the raw evidence?
Ask to see the artefact behind one number in a sample report: the response as returned, the prompt as issued, the timestamp, the session conditions. An agency that measures properly has this on file because its own reporting depends on it. A polished dashboard with nothing behind it is a tool subscription you could license yourself.
8. Do they benchmark actual competitors?
Your visibility in isolation is not decision-useful. What matters is who the model recommends instead of you, and whether that set is your real competitive set or a list of directories and international brands you will never displace. That distinction changes the entire strategy: displacing a competitor is a positioning problem, displacing a listicle is a placement problem.
Good answer: named competitors agreed with you in advance, tracked on the same frozen prompt set, in the same runs.
9. Can their technical team implement the findings?
Most AI visibility problems are not content problems. The recurring causes are structural: indexation faults, redirect and trailing-slash issues that keep commercially important pages out of the valid index, weak or contradictory entity signals, missing structured data, thin third-party corroboration, pages that answer nothing extractable.
An agency that can produce a report but cannot deploy a fix hands you a PDF and a dependency. Ask who writes the code, and whether website work and visibility work are delivered as one engagement or thrown over a wall. Ours are: implementation runs through Bluemind’s development team.
10. Do they remeasure using the same baseline?
The baseline must be captured before any changes are made, and the same prompt set, run count and session conditions repeated afterwards. Anything else is unfalsifiable: any movement can be attributed to the work, and any absence of movement explained away.
Good answer: a documented before-state you receive at the start, and cycle-over-cycle comparisons on identical conditions.
Red flags, condensed
- Guarantees. “We will get you into ChatGPT’s answers” is not a promise anyone can keep.
- A proprietary score with no published method. If you cannot reconstruct the number, no one can audit it — including them.
- One engine only. See the engine-share data above.
- English-only measurement for a Greek-market business.
- Metrics with no source. “Industry data” is not a source.
- AEO sold as a content-volume package. Twenty articles a month is an SEO reflex applied to a different problem.
- A reseller dashboard with a markup. You are paying for a subscription plus margin.
How we work at AEO Agency
Our delivery model is built around the problem above: the measurement is noisy, so the method has to be defensible before the results mean anything.
We start with an audit, not a retainer. Every engagement begins with a paid audit producing a diagnosis and a prioritised roadmap you own, whether or not you continue with us. We would rather lose a client at proposal stage than book a retainer against an undefined baseline.
Cold-prompt protocol. Logged-out, non-personalized sessions. A frozen query set agreed with you and held constant. Multiple runs per prompt. Explicit void-run rules. Mentions, citations and self-citations recorded as three separate signals, with recommendation prompts scored as their own block.
Greek and English, tracked separately. For domestic clients the Greek set is primary; for exporters we run both and report them apart, because they behave as two different markets inside one brand.
Tooling plus manual cross-validation. We use commercial tracking (Otterly) as an input, not as the answer. Every reported figure is cross-checked against the raw responses — which is the only reason we can answer question 7 about our own reports.
A cause taxonomy, not a checklist. Findings are classified into seven cause categories, so every recommendation is attached to a diagnosed reason rather than a generic best-practice list. That classification drives the monthly roadmap, tracked in a structured action system: what shipped, what moved, what is still open.
We implement. Bluemind has been building SEO-first websites since 2017 — and AEO/GEO-first websites since 2025 — with in-house developers and no bought templates. Indexation, schema, entity corrections, architecture and content restructuring are executed by the team that diagnosed them. See our case studies for what that looks like in practice.
No fabricated metrics. We do not report numbers we cannot reproduce. Where the data is genuinely uncertain, we say so and report the range.
What we will not promise
We will not guarantee placement in a specific AI answer, because no one controls that. We will not present a single number as if it were a rank. And we will tell you when the honest read of your baseline is that the fastest path is not AEO at all, but fixing the site underneath it.
Key takeaway
Before hiring an AEO or GEO agency in Greece, ask them to show how they measure AI visibility: a frozen prompt set, repeated runs, non-personalized sessions, mentions separated from citations and recommendations, results reported per engine and per language, raw evidence preserved, real competitors benchmarked, technical capacity to implement, and remeasurement against the same baseline. If they cannot describe that protocol, they cannot prove the result.
AI Visibility Intelligence & Optimization
The ten questions above are not ten separate services. They describe a single loop, and every serious engagement runs it continuously:
Measure → Diagnose → Optimize → Remeasure
| Stage | What happens | Questions it answers |
|---|---|---|
| Measure | Frozen prompt set, repeated runs, non-personalized sessions, per engine and per language. Mentions, citations, self-citations and recommendations recorded separately, with raw evidence preserved. | 1–7 |
| Diagnose | Findings classified into cause categories and benchmarked against your real competitive set — so each gap is attached to a reason, not a best-practice list. | 5, 8 |
| Optimize | Indexation, entity signals, structured data, architecture, content and third-party corroboration — implemented, not recommended. | 9 |
| Remeasure | The same prompt set, run count and session conditions, re-issued against the original baseline. Movement is verified, not asserted. | 10 |
Intelligence without optimization is a report. Optimization without intelligence is guesswork. The loop is what turns one into the other — and the reason we can tell you, cycle over cycle, which change moved which signal.
FAQ
Start with the baseline
If you are evaluating agencies, ask all ten questions before you look at a single price. The method is the product.
