AI response systems are changing how content is found. But instead of serious strategies, the first offers for “GEO rankings” or “AI SEO optimisation” are popping up in many places. Why such promises are mostly nonsense – and what really matters with GEO/LLM Visibility.

Online search as we know it is changing. ChatGPT, Perplexity, Google AI Overviews … AI-supported response systems summarise information, draw on sources and provide direct answers to specific questions. For companies, this means that visibility is no longer only achieved through traditional rankings, but where AI systems understand, categorise and cite content, so it’s no wonder that the first providers are already selling “GEO rankings”. The problem is that there are no rankings in AI answers. Anyone promising this is either selling ignorance – or deliberately misleading. GEO/LLM Visibility works in a fundamentally different way to traditional SEO. And that is precisely why it is worth taking a sober look at what really counts.

Why There Are No “GEO Rankings”

In contrast to Google search results, there are no fixed positions in AI optimisation. Answers are generated dynamically – depending on the question, the context and the database of the respective LLM system. A brand that is cited in an answer today may no longer appear tomorrow. The same question, formulated slightly differently, can already deliver fundamentally different results, which means that visibility cannot be captured in a “ranking”, it manifests itself as mentions, citations or content references. The decisive factor is not where a piece of content appears, but whether it appears at all – and in what context. So anyone who promises a “top 3 ranking on ChatGPT” has either not understood the system or is speculating that customers won’t do it.

SEO optimization based on citability rather than keyword density

While classic SEO reacts strongly to keywords, page structure and backlinks, AI systems evaluate content semantically, section by section and contextually. They do not extract entire pages, but individual text passages. B With content for LLM and GEO optimisation, it is not only the content that counts, but also its comprehensibility and structure.

Effective GEO levers are therefore:

• Semantic clarity instead of keyword focus: Content must be clearly formulated and clearly state technical contexts. Meaning and context are more relevant than the mere repetition of individual search terms.
• Quotable text sections: Each section should be self-contained, technically robust and understandable without additional context. AI systems do not take content page by page, but extract individual passages.
• Structural clarity: Lists, tables, question-answer structures, comparison blocks – all of these facilitate machine processing and increase the probability of content appearing in answers.
• Entities and semantic anchoring: Brands, products and technical terms must be consistently named and logically linked. Different spellings or changing terms for the same concept make semantic categorisation difficult.

Topical Authority: Depth Trumps Breadth

Another key difference is topical authority – the thematic authority of a website within a defined topic area. It is not created by individual optimised pages, but by the systematic and content-based coverage of a topic complex at website level, i.e. the topic area in whose attention cosmos you want to take place should be treated as holistically as possible, with a comprehensible overview and relevant details. Content can be distributed across different page types: product pages, for example, specialised articles, FAQ sections or glossaries. The only decisive factor for GEO optimisation is that they are logically linked and help both search engines and AI systems to recognise connections.

Off-page factors: External sources are becoming more important

The focus is also shifting outside of your own website. Traditional backlinks remain relevant, but for AI optimisation and GEO/LLM visibility, citations without direct links are becoming more important. The mention of a brand, a product or a technical term on external, thematically relevant pages strengthens the perception as an established source – even without an explicit hyperlink: Knowledge and reference sources such as encyclopaedias, specialist portals or Wikipedia. They often serve AI systems as a basis for categorising entities and contexts.

Measurement: Prompt-based rather than ranking-oriented

Because there are no fixed rankings, a different measurement methodology is needed. Instead of tracking positions, realistic, thematically relevant user questions (prompts) are formulated and regularly executed in AI systems. The following questions in particular are analysed: Which brands, content or sources appear in the answers? How often? In what context?

Relevant KPIs include, for example:

• Brand Mentions: How often is your own brand mentioned in AI responses?
• Citation frequency: How often is content used as a source?
• Prompt coverage: In how many of the defined prompts does the brand appear?
• Competitive comparison: How does your own visibility compare with that of your competitors?

This data can be recorded using specialised GEO optimisation tools such as Peek AI or Finseo AI and visualised in dashboards. The decisive factor is that the evaluation is relative, not absolute. And it requires continuous monitoring, not one-off optimisation.

Conclusion: Honest iteration beats empty promises

AI optimisation for more GEO/LLM visibility is not hype, but a logical development in the way content is found and evaluated. However, it cannot be measured and optimised using the same methods as traditional SEO. Anyone who promises “GEO rankings” is ignoring the way the systems work – or relying on the fact that nobody will ask.

Serious work in this area means creating technical foundations, preparing content in a structured and citable manner, building thematic authority and strengthening external signals. Taking into account that AI and LLM searches are constantly evolving in parallel and require continuous iterations. And above all: make transparent what is measurable – and what is not (yet).

Because in the end, it’s not about tricking a system. It’s about preparing content in such a way that it can be correctly understood, categorised and used as a trustworthy source by AI systems. So that the right content reaches the right people – no matter where and how they search.