Decision-makers use AI models to conduct market analyses, compare vendors, and prepare technological feasibility studies. For B2B companies, this development means that industry publications, white papers, and case studies must be specifically structured so that AI models can extract them as reliable evidence. In business-critical contexts, a document’s verifiable authority is a key factor in whether it is cited.
In the age of Generative Engine Optimization (GEO), structured data based on Schema.org plays a central role in ensuring that AI models still perceive a website as a reliable source. Since language models do not read web pages the way humans do, clearly labeled facts help them feed information directly into their knowledge databases. The following article highlights key Schema types and explains why the sameAS attribute is the secret lever for securing the trust of AI systems in the long term.
In an era when internet search has undergone a fundamental transformation, traditional search engine optimization alone is no longer enough. It’s 2026, and we’re in the era of Generative Engine Optimization (GEO). Today, it’s no longer just about ranking well on Google, but about ensuring that AI models like ChatGPT, Perplexity, or Gemini select your content as a trustworthy source and cite it directly in their responses.
The most effective way to achieve this new kind of visibility is by filling knowledge gaps. In this post, we’ll show you how to use reverse prompting to identify blind spots in your niche and create content that’s relevant to AI models.
In traditional SEO, formatting was primarily intended to improve readability for humans. In Generative Engine Optimization (GEO), it is a critical technical factor for data extraction. Language models (LLMs) process information in clusters of tokens. Unstructured continuous text increases the error rate. Studies from 2026 show that targeted Markdown structures can increase the extraction accuracy of AI systems by up to 40%. Therefore, anyone who wants to be cited uses structure as an anchor.
One notable trend in the GEO sector over the past few months is that ChatGPT, Perplexity, and Google AI Overviews are increasingly embedding video snippets directly into their responses. Videos are no longer an “unreadable” format for AI models, but rather a structured data source. For brands, this means that video GEO is an effective tool for establishing themselves not just as a text link, but as a visual authority. In the following text, you’ll learn how to optimize your videos for generative search.
Many modern websites use JavaScript to display content. What looks good to users is often a problem for AI crawlers. While Google can usually handle JavaScript well, bots from OpenAI or Perplexity frequently struggle with dynamic content. If the AI can’t read your text, your brand won’t appear in the results. In this post, we explain the technical hurdles and how to overcome them.
If you ask Google a question today, you’ll often get two different answers, depending on whether you type it into the classic Google search (AI Overviews) or ask the Gemini chatbot directly. While both systems are based on the Gemini-3 model family, their “mission,” data sources, and objectives differ significantly. For brands, this means that appearing in the AI Overviews doesn’t automatically mean you’re embedded in the Gemini assistant’s memory. In this post, we analyze why this discrepancy exists and what it means for your GEO strategy.
Customer reviews often make all the difference: Users find a product on Google, read the reviews, and then decide whether or not to buy it. Reviews are a classic “bottom-of-the-funnel” tool for conversion optimization. But in the age of Generative Engine Optimization (GEO), this is changing. Nowadays, reviews often determine whether a brand even appears in an AI’s “consideration set.” When a user asks ChatGPT or Perplexity, “Which e-bike is the most reliable for commuters?” the models scan review platforms, forums, and test reports in milliseconds. Any brand that isn’t mentioned there or receives negative feedback won’t be recommended by the AI.
For a long time, Generative Engine Optimization (GEO) was purely about text. But with the major model updates in the spring of 2026, the playing field has expanded. Systems like Google Gemini, GPT-4o, and SearchGPT no longer function merely as “reading machines,” but as multimodal observers. They “see” graphics, “hear” podcasts, and “understand” video content at the frame level to embed it directly into their generative responses. For brands, this means that those who optimize only text lose their visual and auditory presence in AI search.
Optimizing for AI search engines (Generative Engine Optimization) can be particularly complex for companies operating internationally. One of the most pressing questions for international companies is: How does the source base for AI responses differ across various markets?
