Every time I sit down with a business to talk about GEO, the first objection arrives in almost exactly the same words: “this all sounds lovely, but how do I know any of it works?”. And they are right to ask. If something cannot be measured, it is not work, it is a promise. And promises, let’s be honest, you have heard plenty of. Am I wrong?
The good news is that visibility in the answers of answer engines can be measured. Properly, with numbers, with repetitions, with comparison. And that measurement is exactly what separates a serious approach from marketing by slogan.
The short version, for those in a hurry: we check whether the models mention you by asking them systematically with the questions your clients ask, recording whether and how you appear, and comparing your picture against competitors and against your own past self. But let me walk through how it works, because the substance is in the details.
Why this visibility suddenly matters so much
Before the “how”, the “why now” deserves a moment. Search behaviour is changing faster than our habits can keep up with. A Pew Research Center study in July 2025, based on the browsing data of 900 adults and nearly 69,000 searches, found something decisive: when an AI summary appears in the results, users click a traditional search result in just 8% of visits, while without a summary the figure is nearly double, 15%. And the most striking part, the one I had to read twice: the source link inside the summary itself is clicked in just 1% of visits.
What does that mean for you? That the battle has moved, and nobody asked our permission first. It is no longer enough to be one of ten results. If you are not inside the summary the user reads, an entire and growing category of potential clients doesn’t see you at all. They don’t even skip past you, because to skip past something you first have to see it. That is why measuring your presence there isn’t a luxury, it is the thermometer of your visibility.
First we find the questions your clients actually ask
GEO visibility is not measured with one generic prompt, however convenient that would be. It is measured with the real questions a prospective client would ask: “who builds an eshop in Thessaloniki?”, “best SEO company for a small hotel”, “who should I trust to manage short-term rentals”. In other words, in the customer’s language, not the language of your brochure.
So we build a representative set of such questions for your field and your audience. That set becomes the benchmark, the fixed point everything else stands on. Without it, any measurement is arbitrary.
And then we ask exactly where your clients are asking
One model is not enough. Your clients ask ChatGPT, Perplexity, Gemini, and they see Google’s AI Overviews. Each one synthesizes in its own way and draws from different sources, like four people who read four different newspapers and are then asked the same question.
So we check your presence across everything that matters for your audience. Because one may mention you while another ignores you, and that, believe me, is useful information on its own.
We care about the “how”, not only the “whether”
The question is not only “did it mention you?”. It is also: did it mention you accurately or with wrong details? Did it place you in the right category? Did it present you as a trustworthy option or as an afterthought, the name added at the end of the list to fill it out? Did it connect you with the right area and the right services?
A model that mentions you incorrectly is almost as costly as one that ignores you. The quality of the mention matters as much as its existence. So we record specific metrics: mention rate (how often you are mentioned), citation rate (how often you are given as a source), and your position or prominence within the answer.
A number on its own says absolutely nothing
A number with no reference point is noise with digits in it. That is why measurement has two axes.
The first is competition: who the models mention instead of you, and why. Here we measure share of voice, your share of presence against competitors across the same set of questions. The second axis is time: how your picture changes before and after the work is done. That second comparison, the one against your own earlier self, is what turns GEO from a promise into a measurable result.
Let’s also look at what your visitors actually do
Presence in the answers of answer engines leaves traces in your own data too: visitors arriving from AI platforms, queries that mention your business by name, shifts in behaviour after a mention. Small footprints, but perfectly legible to anyone who knows where to look. By cross-checking what the models say with what visitors actually do, the picture becomes complete rather than theoretical.
Why one measurement is not really a measurement
This is perhaps the most misunderstood point in the whole story, and thankfully there is now research that documents it. A University of St. Gallen paper with the telling title “Don’t Measure Once” showed just how unstable answer engine results are: the Jaccard similarity of the sources cited from one day to the next averages 0.34 to 0.42. And now for the genuinely awkward part… When you submit the exact same question on the same day, removing time from the equation entirely, the number does not improve: 0.32 to 0.43. Practically identical. That is the finding, and it is more unsettling than a simple decline would have been: the instability is not caused by a day passing and the world moving on. It is caused by the probabilistic nature of the models themselves. What a model cites now it may not cite ten minutes from now, with absolutely nothing having changed.
The researchers’ conclusion is entirely practical: one measurement isn’t enough. They recommend at least 7-8 repetitions per prompt and an observation window of 10 to 28 days for a reliable estimate. Which is exactly why anyone who promises “I asked ChatGPT once and here’s the result” isn’t measuring. They are taking a photograph of a random moment and serving it to you as a portrait. The value comes from tracking the trend, with enough repetitions that the noise becomes signal.
We run this through our own platform
None of this is theory, nor manual work every single time, because if it were, we would do it once and then forget about it, like everyone else. We run it through MS-Tools, the proprietary diagnostics platform we built and use daily with clients.
It measures across multiple answer engines, breaks the results down per engine, gives a GEO readiness score, proposes tailored questions and specific advice, keeps history and trend, and turns the findings into tracked tasks and a branded client report. It is not a generic off-the-shelf tool, it is built by a practitioner for real work. That is simply how I work.
And so measurement becomes result
This methodology is the foundation of our work at MS-Logic: we do not promise vague “AI visibility”. We measure where you stand, identify what holds you back, and build the signals models need in order to recommend you, while tracking the change.
Remember the question we started with, the “how do I know any of it works?”. Here is how: with numbers that get measured again. Want a GEO visibility check for your business, with a concrete picture of where you appear today in the answers of the answer engines? Contact us through the form, or at sales@mslogic.gr.