Optimizing AI Search & GEO for TYPO3 Websites: From Win the Click to Win the Answer

Traditional SEO follows a familiar principle: users enter a search term, Google provides results, and companies try to rank as highly as possible with relevant pages.

AI-powered search fundamentally changes this process.

Individual keywords are becoming specific questions and personalized research conversations. For example, “CRM software comparison” could become a much more specific query: “I’m a mid-sized company looking for CRM software, preferably open source. Which solutions would be suitable for me?”

The AI processes these requirements, draws on different sources, and formulates a relevant answer directly.

For companies, this changes one thing above all: The first interaction with a brand no longer necessarily takes place on the company’s own website. By the time users arrive there or speak to sales, much of their research may already have been completed.

Generative Engine Optimization, or GEO for short, addresses precisely this shift. The goal is to build a digital presence in a way that enables AI systems to understand content, classify it correctly, and consider it as a source or recommendation for relevant queries.

Unlike traditional SEO, this is not just about keywords or achieving the best possible position in a list of search results. AI systems combine information from different sources and formulate their own answers.

For a brand to appear in those answers, several signals need to work together: The website needs to be technically accessible, its content needs to demonstrate genuine expertise, and a clear, credible picture of the brand should also emerge beyond its own channels.

What AI cannot read, it cannot cite.

That is why many familiar SEO fundamentals remain important: clean source code, clear structures, functioning links, and technically accessible content.

TYPO3 websites already have a solid foundation for this. The TYPO3 Core supports, among other things, canonical URLs, hreflang, metadata, and XML sitemaps. Many of the technical elements that are important for SEO therefore also provide the foundation for GEO.

Another important building block is structured data. Markups based on schema.org can be used to label information about companies, products, or services in a way that makes it easier for machines to recognize and classify.

In TYPO3, for example, this can be implemented using the actively maintained Schema.org extension by Brotkrueml, which Frank recommends in the webinar. It enables corresponding JSON-LD markup to be integrated into a website, making content more structured and accessible to AI systems.

Other approaches, such as LLMS.txt and WebMCP, are also becoming increasingly relevant. However, both are at different stages of development and should not be viewed as quick GEO solutions. WebMCP in particular is still experimental, but it illustrates how websites could increasingly adapt to being used by AI systems in the future.

Even as more and more research takes place directly within AI systems, high-quality content remains crucial. After all, AI needs sources from which to draw its answers.

What matters is not quantity, but substance.

Instead of interchangeable marketing phrases, companies need concrete answers, reliable information, clear structures, and genuine expertise on their core topics.

Content is particularly useful when individual statements can easily be extracted — for example, through clear headings, tables, or well-structured sections.

AI systems do not only look at what a company says about itself on its own website.

Industry publications, specialist portals, review platforms, and forums can also help AI systems classify a brand and validate claims.

This makes a consistent brand image across different touchpoints all the more important. The more clearly a brand is associated with specific topics and attributes, the easier it is for AI systems to classify it.

GEO therefore does not end with the website. Brand building, PR, and external mentions also influence the picture that emerges of a company.

Before implementing individual GEO measures, companies should first understand where they currently stand.

This includes taking a technical look at the website: Can AI systems access the relevant content? Are crawlers being blocked? Is important information structured properly?

It is also worth establishing a baseline for the company’s visibility in AI search. Beat recommends using a representative set of relevant prompts for this purpose. This makes it possible to track which questions trigger mentions of the brand, how it is described, and which competitors are mentioned instead.

Only then can a meaningful action plan be developed. Perhaps the greatest opportunity lies in the technical setup. Maybe reliable content on a core topic is missing, or the brand has little presence on relevant third-party websites.

The key is to prioritize measures based on impact and effort rather than chasing supposed GEO hacks. GEO is not a one-time optimization, but a long-term process.

GEO does not change all the rules of the game. Many fundamentals remain the same: clean technology, high-quality content, and a clearly positioned brand.

What is changing above all is where visibility is created.

Alongside rankings and clicks, it is becoming increasingly important whether a brand appears in AI-generated answers — and in what context it is mentioned.

Companies that want to prepare for this shift do not need a short-term GEO hack. They need a website and a brand that are equally understandable to people and machines.