Structure, not slogan: the methodological criteria that separate a GEO agency from an SEO agency with “AI” added

A Polish B2B company that starts looking for a partner in AI visibility quickly runs into the same problem: most agencies offering "GEO" or "AI optimisation" cannot explain what a RAG pipeline is, have never run a single citation test in an AI engine, and have no tool that measures brand visibility in generative responses. The offer exists. The competence does not.

This is not a question of bad faith. It is the result of a market that reacted to growing interest in GEO faster than it managed to build real skills. Adding the word "AI" to an existing SEO service is easy. Building a process that actually influences how a language model constructs a response, and who it cites in that response, requires a different working architecture.

Below I describe the structural and methodological criteria that allow you to tell one from the other. This is not about credentials or case studies that can be fabricated. It is about things that either exist in an agency's process or do not.

GEO is not SEO rewritten for a new keyword

GEO, or Generative Engine Optimization, is described as the evolution of SEO in the AI era, covering optimisation for engines such as ChatGPT, Perplexity and Gemini. That definition is correct but insufficient as a criterion for evaluating an agency. Any company can repeat it.

The problem starts when you ask about the mechanics. Classic SEO optimises for a ranking algorithm that evaluates pages according to signals such as links, keywords and load speed. A generative engine works differently: it retrieves text fragments from multiple sources using a RAG (Retrieval-Augmented Generation) mechanism, processes them with a language model, and constructs a response. Whether a brand appears in that response depends on whether its content is accessible to retrieval, whether it is written in a format the model will treat as an answer to the question, and whether the brand entity is consistent and recognisable in the knowledge graph.

None of those mechanisms is a derivative of Google ranking position. An agency that says "we will optimise you for AI the same way we do for SEO, just with new keywords" does not understand the layer in which GEO actually operates.

Four criteria that reveal real competence

The points below are not a wish list. They are concrete things you can verify in a conversation with a potential partner before signing a contract.

• RAG awareness and retrieval architecture. A partner should be able to explain how a generative engine selects fragments for a response, what it means for content to be "retrievable", and how document structure affects whether the model selects it. If they cannot do this without reaching for generalities, they are not working at that level.
• Entity integrity. A brand must be consistent as an entity across different sources: name, description of activity, location, industry associations. Language models build a representation of a brand from many signals simultaneously. An agency that has no process for auditing and maintaining entity consistency has no influence over how the model "understands" a client's brand.
• Answer-oriented content architecture. GEO content is written to directly answer the question a user might put to an AI engine. This does not mean "write long articles with a high keyword count". It means structuring text so that retrieval can extract a fragment that is itself a complete answer. That difference is visible in every piece of text. It can be checked.
• Testing in live AI engines. A genuine GEO agency regularly checks how a client's brand appears in ChatGPT, Perplexity and other engines in response to specific queries. Not as a one-off audit, but as part of the working cycle. If an agency does not do this systematically and has no tool or process for it, there is no basis for optimisation.

Why off-site signals carry different weight than in SEO

In classic SEO, external links matter, but their impact is well calibrated and measurable. In the context of AI visibility the picture is less clear-cut. Practitioner discussions suggest that off-site signals may carry considerably more weight in AI search than in classic SEO, though the mechanism of that influence is not fully documented.

What this means in practice: brand mentions in trade articles, reports, company profiles and content published by others, not only on the brand's own site, influence how a language model builds its knowledge of that brand. An agency that focuses exclusively on content on the client's own site and ignores external signals is working on half the layer.

This is one of the reasons GEO is not simply "content marketing with AI". It requires coordination between what a brand publishes and what other sources say about it. An SEO agency that has added "AI" to its offer usually has no process for managing that dimension.

Entity integrity as a foundation, not an add-on

Language models do not index pages. They build entity representations from what they find across many places at once. If a company has different descriptions of its activity on LinkedIn, in Google Business Profile and on its own site, if its name is spelled differently across different sources, if consistent location and industry signals are missing, the model has difficulty identifying that company unambiguously.

This is not an abstraction. A company whose entity is inconsistent may not appear in AI responses even when it is objectively a better match for a query than a competitor with a better-calibrated entity.

An agency that does not conduct entity integrity audits and has no process for maintaining them has no influence over that layer. A useful question to put to a potential partner: "How do you verify the consistency of a client's entity across different sources, and how often do you do it?" The answer will tell you more than any presentation.

"Answer-first" architecture versus traditional content marketing

Traditional content marketing produces content that is valuable, engaging and positioned around keywords. That still makes sense in the context of classic search. In the context of GEO it is not enough.

A generative engine is not looking for a "valuable article". It is looking for a fragment that is an answer to a specific question. Answer-oriented content architecture means that every section of a text should be written so that it can be extracted from context and used as a standalone answer. That requires a different approach to heading structure, paragraph length and the way sentences are formed.

Worth mentioning here is the E-E-A-T framework, which according to Google's descriptions is used to evaluate content and distinguishes between someone who has genuine expertise in a field and someone who simply writes about a topic. Language models, trained partly on web data, build similar distinctions into their representations of sources. Content written by someone who understands a subject has a different structure from content written purely for an algorithm.

An agency that cannot show how its content is structured for retrieval, and has no process for verifying whether that content actually appears in AI responses, is operating at the level of content marketing, not GEO.

Testing in live engines as a necessary condition

This criterion is the simplest to verify and the most frequently unmet. Ask a potential partner: "Show me how you test a client's brand visibility in ChatGPT or Perplexity." If the answer is "we go in manually and check" or "we use an SEO tool for that", this is not a GEO agency.

Systematic testing of visibility in AI engines requires a structured process: a set of test queries matched to the client's purchase intent, a regular testing schedule, a way of recording results and tracking changes over time. It also requires an understanding that results can vary between sessions and models, which is itself diagnostic information.

Unomage runs this layer of testing through its own platform, available at platform.unomage.com. This is not a feature added to an existing SEO tool. It is a tool built for the specific requirements of AI visibility, giving clients direct insight into how their brand appears in generative responses. The difference between an agency that has such a tool and one that does not is measurable: one can show data, the other can only tell stories.

What this looks like in practice on the Polish B2B market

A Polish B2B company looking for a GEO partner enters a market that is at an early stage of maturity. Most players who appear in search results under "agencja GEO" are SEO agencies that have modified their offer, not their process. That is not an accusation; it is an observation about the state of the market.

For a buyer, this means that verification must rest on structural criteria, not declarations. An agency that genuinely works at the GEO layer should be able to:

show a tool or process for testing AI visibility, explain the mechanics of RAG without reaching for generalities, conduct an entity integrity audit for the client and identify specific inconsistencies, present examples of content written for an "answer-first" architecture and explain why it is structured that way.

If none of these things exists as a documented element of the process, "GEO" in the offer is a label, not a competence.

GEO (Generative Engine Optimization) is described as the evolution of SEO in the AI era, covering optimisation for engines such as ChatGPT, Perplexity and Gemini.

Source: geekweek.interia.pl

Verifying a partner: what to check before the conversation

Before entering a conversation with a potential GEO partner, it is worth checking a few things that are publicly available:

• Does the agency itself appear in AI responses to queries related to GEO? A company that is not visible in AI for its own services is unlikely to build that visibility for a client.
• Does it publish content with an "answer-first" structure, or rather classic blog articles with a high keyword count?
• Does it have its own tool or a documented process for testing visibility in generative engines?
• Can it explain what distinguishes optimisation for retrieval from optimisation for ranking?

These are questions whose answers can be found before the first conversation. If an agency's public content answers none of them, the conversation probably will not either.

Unomage is a registered Polish spółka z o.o. (KRS 0001152225, NIP 9512614313, REGON 540770406) based in Warsaw, specialising in GEO and brand visibility in AI for B2B companies in Poland and the CEE region. The platform platform.unomage.com is publicly accessible and verifiable, which is a necessary condition for content that is to be indexed and cited by AI engines.

This article was created with the help of the Unomage AI platform.