Is it even possible to get into AI answers?

A PR professional I know said recently that he doesn’t believe a brand can deliberately get into AI answers. An acquaintance of his who works in SEO told him it simply isn’t possible. And they aren’t talking nonsense. Within their own professional framework, they’re right.

There really are plenty of miracle-cure salesmen around AI today. Some promise guaranteed AI recommendations. Others claim they can “insert” your brand into the models’ training data, or try to sum up a company’s AI presence in a single metric. These are inflated promises. Doubt is the justified response to them. We are doubters ourselves.

So the problem isn’t the skepticism. The problem is that many people are still using the old map. Seen through an SEO lens, “getting into the model” sounds like buying a permanent slot in a black box. That genuinely is impossible.

Except that isn’t the task.

The question is not whether you can get into a model’s memory, but how you can raise the odds that an AI system finds you, recognizes you, and uses you. That is no longer a matter of belief but of distinct mechanisms. And that is precisely why we’re not talking about a single problem, but about three different layers.

Classic SEO solves a well-defined task: it optimizes a website’s position in a search engine’s results list. A valuable and mature craft, but its object remains the same: your ranking in a list of links.

Machine visibility, however, no longer consists of one problem, but of three different ones.

The first question is whether an AI system, searching the live web, uses your content when assembling its answer. That is decided immediately, in real time, at the moment of answering.

The second question is whether the model, with no internet connection, can recall your brand from its own parametric memory. That is the level of memory.

The third is whether an AI agent can interpret your machine-readable product or service catalog (assuming it’s readable at all), and can execute an actual transaction on the user’s behalf. That is the level of the transaction.

An important clarification belongs here, because this is where it’s easy to slip. The technical foundations of the first layer — server-side rendering, transparent structure, structured data — do partly coincide with the toolkit of good technical SEO. Denying that would be as much of an error as claiming that SEO alone solves machine visibility.

The real difference begins at the other two layers. Shaping a model’s memory and serving AI agents are tasks that traditional SEO was simply never designed for. Not because it’s a bad method, but because it solves a different problem.

SEO optimizes keywords and builds links so that you climb higher in the results list. We create content that directly answers a question, and that an AI system can easily recognize, lift out, and cite.

SEO works so that the search engine finds you. We work so that the model also understands who you are, what you do, and which questions you are a credible answer to. The foundation of this is consistent structured data and a unified entity model — meaning the same information appears everywhere, always, in the same words. In AVE Studio’s case, for example, it very much matters whether I say “machine brand building” or “AI brand building” across different platforms. Say it one way here and another way there, and a human will still understand — but a machine may get confused.

SEO focuses primarily on optimizing the website. We build the entire machine identity: a single consistent, verifiable, and credible entity from which AI systems not only read information, but over time can also learn.

Picture search as a library. Classic SEO works to get your book onto a good shelf, where the reader can easily find it. The first layer of machine visibility is about something else: when someone asks, the librarian quotes from your book.

This is the most tangible and most easily verifiable layer, because it plays out in front of our eyes every day. ChatGPT’s search, Perplexity, and Google’s AI overviews search the live web at the moment of the query, then assemble their answer from the sources they find. They don’t cite from nowhere: they work with what they can find and interpret.

That is why a website must be readable in technical terms too. Server-side rendering, transparent structure, a question-and-answer format, and properly embedded structured data all help an AI system not just find the content, but understand it — and cite it when needed. This is what we call generative engine optimization (GEO), and in its question-level form, answer engine optimization (AEO). These are not tricks; they mean the content adapts to how the system works.

Our own research supports this. Of the nine Hungarian companies we examined in June 2026, eight appeared at least once in a relevant AI answer. It’s important to stress that this was not an interventional study but an observation. It doesn’t prove that a given method is guaranteed to work; it proves that this layer already exists today, and that Hungarian companies are capable of appearing in it.

The ninth case was especially instructive. A hotel was left out of the answers not because the AI “didn’t like it,” but because its generic name collided with countless other hotels, while the big booking portals crowded its own website into the background. That is not an impenetrable obstacle but a diagnosable, fixable problem. This is precisely what separates professional work from the promise of magic.

The first layer, then, is not about making the AI do something. It’s about making sure that when the system looks for answers, our content is among the ones worth using. But it will only do so if that content is well structured, clearly identifiable, and machine-readable. If our content fails those criteria, we become invisible to the machine.

This is the layer most skeptics consider impossible from the outset. How could a language model “know” a brand when it isn’t searching the internet?

Yet the question is not one of faith but of engineering. The model’s parametric memory exists, it is measurable, and while it cannot be steered directly, its inputs can be influenced.

A simple analogy shows the difference well. The first layer is like someone looking something up before every question. That is rented reach: it always depends on the current search. The second layer, by contrast, is like an expert’s knowledge. They don’t know something because they just looked it up, but because they encountered it from enough credible sources, enough times.

Large language models work exactly the same way. During training, entities are built up from credible, mutually consistent records, from knowledge graphs, and from many sources that reinforce one another. You cannot “load” a company into a model’s memory with a single switch. What you can do is work so that when the next training cycle happens, the model encounters a consistent, clearly identifiable, and credible picture.

Checking this is simpler than many think. Turn web search off and ask the model about a brand. Whatever it answers then, it is recalling from its own parametric memory. This is what we call owned memory. The work that supports it is knowledge engine optimization (KEO).

It’s important, though, to draw the line clearly. No one can guarantee that a given brand will make it into the next model version’s training data, or with what weight it will appear in its memory. Our working hypothesis is more modest and professionally defensible: a credible, consistent, and machine-interpretable presence significantly raises the odds. That is not something to believe — it’s something to re-measure at every new model generation. And that is exactly what we do.

What does this mean in practice? First, we create or clean up an authoritative entity record — a Wikidata entry, for example — which becomes the brand’s machine identifier. Then we build a unified sameAs network so that every credible source points to the same entity. We fix the canonical facts (the ones accepted as the authoritative source) — who we are, what we do, when we were founded — and then present them consistently, in the same form, across every significant surface. Finally, we align important publications with the models’ known knowledge cutoffs — that is, we publish them when and where they have a real chance of entering the next training cycle.

The results of our own pilot study are telling here too. Among the nine companies examined, the model recalled most reliably not the largest ones, but those with a more distinctive and unambiguous machine identity. It recognized a mid-size winery and a small shoe brand with a strong heritage without difficulty, while it could recall nothing about a larger B2B software company with a generic name.

The most common deficiency was surprisingly simple: six of the nine companies had no proper, authoritative entity record at all. That is not a technological limit but a fixable condition.

The second layer, then, is not about “getting into” a model’s head. It’s about making sure that when a model learns, there is something about us for it to learn.

This is the youngest of the three layers. It is not yet a mature ecosystem but an infrastructure taking shape. Which is exactly why it demands the most careful language.

The question, however, stays the same: what happens when an AI no longer just answers or remembers, but also acts?

The first signs are already visible. AI agents have appeared that don’t merely look up information but can carry out tasks: compare products, make bookings, place orders, or start purchase flows on the user’s behalf.

For that, though, the business has to be prepared as well. An AI agent doesn’t “shop” the way a human does. It doesn’t watch ads, doesn’t decide based on design, and doesn’t browse through the website. It reads structured data: prices, stock information, product descriptions, delivery terms, and every piece of information it can interpret by machine.

So the question is no longer whether an AI can find your website, but whether it can interpret it. Can it identify your product? Does it understand the price? Can it assess the stock? Can it execute the transaction? This is what we call machine buyability.

This field is still at an early stage, which is why it’s especially important not to overpromise here. Agentic commerce is not an AVE Studio invention but an industry direction. The standards are still forming, the platforms keep evolving, and today no one can say how quickly purchases carried out by AI agents will become commonplace.

That, however, does not mean it’s too early to prepare.

The technical foundations — machine-readable catalogs, structured product data, standard APIs, and a consistent entity model — can be built today. When the infrastructure becomes widely available, these will no longer be a competitive advantage but an entry requirement.

Which is why the third layer is not really about tomorrow. It’s about not having to start laying the foundations at the moment AI agents show up in purchase flows en masse.

This is how the logic of the three layers becomes complete. First, the AI finds you. Later, it recognizes and remembers you. Finally, it becomes capable of doing business with you.

This is where professional work and the marketing gimmick part ways. And this is also where skepticism’s most important objection dissolves.

When someone hears that a company will “get into AI answers, guaranteed,” they are right to doubt it. We would too. A promise like that cannot be kept.

What can be claimed is far more modest — and far more precise.

The difference is the same as between the work of a good PR professional and the promise of a guaranteed front page. The first is professional work: credible data, ordered entities, consistent sources, strong technical foundations, and disciplined measurement. The second is a simple marketing gimmick.

That is why we don’t claim that how models work is predictable or controllable. We claim that their inputs can be organized, improved, and measured. That is the fundamental difference.

Every new model version is a new measurement point. You can examine again whether a brand appears in the answers, whether the model recognizes it without web search, and whether an AI agent can interpret its offering. These are not matters of belief but observations.

So the skeptic is not wrong to reject the inflated promises. Where he is wrong is in concluding from them that nothing can be done.

Between those two claims lies an enormous difference.

You cannot guarantee the outcome.

But you can systematically raise the odds of the outcome.

If all this really works, the obvious question follows: why isn’t everyone already doing it?

Because machine visibility is not the extension of a single profession, but the intersection of three different fields. You have to understand brand building, machine-interpretable information structures, and the implementation of the technical systems AI can build on — all at once.

The first pillar is marketing. Not advertising, but the ability of a brand to articulate clearly what sets it apart from its competitors, and which questions it is a credible answer to.

The second is entity building. AI systems need not websites but unambiguously identifiable entities. That requires consistent structured data, unified references, and a machine-interpretable knowledge graph.

The third is engineering. The content must be not only well written but technically accessible. Server-side rendering, structured data, standard APIs, and transaction-ready systems are as much a part of this work as the content itself.

In most organizations these three competencies live in separate teams. They rarely work toward a shared goal, which is why machine visibility mostly stays fragmented. Whoever develops only one of the fields loses a significant share of the value hidden in the other two.

That is why we speak of a new craft.

Not because marketing, SEO, or software development have suddenly become obsolete, but because a new audience has appeared. For the first time, a player stands between the brand and the buyer that is not human: it answers, it remembers, and increasingly, it acts.

This change doesn’t replace the older professions; it adds a new task to them. The goal is no longer only that people understand the brand, but that machines interpret it correctly.

Which is why we don’t try to persuade the machine.

We make sure that when it decides, it forms the most accurate picture of us that exists.

The three layers — answer, memory, transaction — together form the framework we call Machine Equity. It is not a new name for SEO, but a new way of thinking about how a brand becomes visible, recognizable, and ultimately choosable in the age of artificial intelligence.