With traditional SEO, we work with the keywords people type into search engines. With AI responses, however, we also need to understand the broader context – the very context AI platforms consider when generating answers. To address this, Visibility built a proprietary tool to analyze these connections, known technically as query fan-out, helping us uncover new topics, sub-questions, and content opportunities.

Client

Visibility is an award-winning digital marketing agency that helps businesses grow through SEO, performance campaigns, and content, supported by analytics and solid strategy. It operates in a sector where clients evaluate expertise, experience, case studies, and the ability to deliver measurable results when choosing a partner.

Services
SEO

Client
Visibility

Team
SEO specialist – Dávid Škola

When AI Addresses More Than a Single Question in Responses

In traditional SEO, we look at keywords users enter into search engines. AI platforms, however, work with a broader context when constructing responses. They can break a single query down into multiple related topics, follow-up questions, comparisons, or sources that help them generate the final answer. This concept is referred to as query fan-out – the expansion of an initial search query into multiple sub-questions and related contexts.

For SEO, this marks a significant shift. Focusing solely on the primary keyword or a single user question is no longer sufficient. For content to stay relevant within AI responses, it must address the broader context around a topic: related queries, comparisons, decision criteria, and terms that AI platforms may take into account when providing an answer.

A Proprietary Tool for Analyzing Broader Topic Context

At Visibility, we developed a proprietary tool that helps identify additional questions and thematic areas AI platforms may address when building a response. Starting from a core question or topic, the tool analyzes related phrasings, sub-questions, comparisons, and concepts that frequently emerge around it.

We can run this analysis across various AI platforms, such as ChatGPT or Gemini, and customize it by language, country, or iteration frequency. This is crucial because AI responses can vary from run to run. Multiple iterations help us distinguish random outputs from topics that surface consistently.

The result is not just a technical data dump, but a practical baseline for content briefs, existing content optimization, and planning new articles.

Better Content Through Understanding AI Search

The tool’s output helps us define precisely what content needs to cover so it does not remain too narrow. It reveals which subtopics require explanation, where to add an FAQ section, where a comparison is needed, and which related questions belong within a single thematic piece. At the same time, it helps prevent unnecessary duplication – identifying cases where creating multiple similar articles can be replaced by one stronger, more comprehensive piece of content.

For daily SEO execution, this represents a practical step forward. Keywords tell us what users search for. This tool complements that view by showing what broader context matters for AI responses. Consequently, we can provide copywriters with clearer briefs, structure articles more effectively, fill content gaps, and plan topics that are useful for users, readable for search engines, and relevant for AI platforms.