Visibility in ChatGPT & Co: the ultimate guide for your B2B website

The digital landscape is currently undergoing what is arguably its most significant transformation since the invention of Google: user search behaviour has changed fundamentally – including in the B2B sector.

More and more people are relying on zero-click searches: when looking for solutions or providers, they bypass search engines such as Google and turn directly to AI systems like ChatGPT, Gemini, or Perplexity. And those who continue to use search engines? They increasingly rely on the AI Overview or the Search Generative Experience (SGE), which integrates Generative AI directly into search results.

For companies and their marketing teams, this development does not mean that traditional SEO can be ignored (more on this later), but it does increase the urgency of measures to improve visibility within established Large Language Models (LLMs). The new keyword is "AI visibility" – namely: how likely is it that your brand, web content, and services will be recognised, understood, and subsequently included in responses by generative AI models?

To achieve this, web content must be accessible, perceived as relevant, and classified as trustworthy. Many companies are not prepared for this with their current digital offerings. They risk becoming digitally invisible – and face a significant drop in traffic.

How do you ensure that web content is accessible to ChatGPT & Co?

Technical accessibility is ensured by controlling crawler access. The key to this? The robots.txt file. It governs whether indexing by AI-relevant bots – such as OAI-SearchBot, GPTBot, and ChatGPT-User – is possible. It is also important that content is available server-side as HTML, since OpenAI's crawlers cannot execute JavaScript. Anything that is not present as static HTML in the source code remains invisible to AI. Fast loading times are equally important.

Another control mechanism is the LLMS.txt file, with a clearly structured layout and unambiguous, static URLs. It selectively lists URLs and content that are relevant to Large Language Models. However, an LLMS.txt file is no guarantee of good visibility, as some bots reportedly largely ignore it.

What can I do to be mentioned more frequently by ChatGPT & Co?

In addition to machine-readable formatting and semantic clarity, AI models evaluate the currency and trustworthiness of content positively. This is comparable to traditional SEO, where Experience, Expertise, Authoritativeness, and Trustworthiness (E-E-A-T for short) have long served as a guiding framework.

To enhance the trustworthiness and credibility of web content, for example, the use of source citations is recommended. An imprint and a privacy policy also provide the necessary transparency. Even stronger signals include mentions in reputable specialist media or on authoritative third-party sites (e.g. Wikipedia), backlinks from trusted domains, and positive mentions on social media, in podcasts, on review platforms, or in forums.

Repurposing content is also beneficial – for instance, covering a topic across podcasts (note: do not forget transcripts), blog articles, and external trade publications. The chances of being cited are further increased by providing exclusive content – above all, the publication of studies or benchmarks based on proprietary surveys, analyses, or data.

How do I ensure that my web content is correctly understood and reproduced?

For B2B companies with complex content and offerings, "structured data" is essential. Structured data consists of standardised information embedded into a website's HTML code via special markup languages such as JSON-LD, Microdata, or RDFa. It provides web page content with a clear structure so that search engines, LLMs, and AI bots can better understand context and meaning. The vocabulary of Schema.org – developed by Google, Microsoft, Yahoo, and Yandex – has established itself as the standard for this purpose.

To ensure that content – for example in a company blog – is citable, it should be divided into concise, logically structured sections, each addressing one key point. This structure makes it easier for LLMs to capture and process content. Short summaries, tabular comparisons, and a question-and-answer format also make it easier for LLMs to extract information. The wording of content, including the formulation of questions, should mirror the language of the target audience (i.e. AI users) as closely as possible. For images, alt tags and image descriptions should not be overlooked.

How can I measure my brand's and content's AI visibility?

To measure the success of GEO (Generative Engine Optimisation) or LLMO (Large Language Model Optimisation) efforts, the use of specialised AI search monitoring tools is recommended – such as Peec AI, OtterlyAI, or the Agent Analytics from Profound. productrank.ai is a good starting point to initially check whether and how you rank for certain queries in ChatGPT, Anthropic, Perplexity, and Gemini. The list of citations also helps identify potential third-party sites for media partnerships.