Query Fan-Out: The Technology Behind Google's AI Mode

Imagine asking Google a complex question and receiving a synthetic, relevant, and personalized answer without having to browse dozens of links or even type multiple queries. This is the ambition of Google's AI Mode, which relies on a technique called "query fan-out". This major innovation in how we conceive online search, currently available for free only in the United States (and likely not for many months, even years, in France), seems to be based on a patent published in August 2024, recently dissected by Michael King, an American SEO expert. But let's look at all this in detail.

The Patent Behind Query Fan-Out

The query fan-out technology apparently finds its origins in patent US20240289407A1, titled "Search with Stateful Chat", published by Mahsan Rofouei, Anand Shukla, Qing Liang et al, on August 29, 2024. At least that's what Michael King, an international SEO specialist known notably for being one of the first to publish an article on the Google Leaks affair, claims in this LinkedIn post.

This patent describes a nine-step process that fundamentally modifies content discovery and ranking mechanisms.

Here is this 9-step process:

This approach marks a break with traditional search where a query generates a single set of results.

How Does Google's Query Fan-Out Work?

Very similar to the mentioned patent, "query fan-out" is a method that breaks down a user query into multiple specific sub-queries, covering different aspects of the initial topic. As Google officially explains: "AI Mode uses our query fan-out technique, breaking down your question into sub-topics and issuing a multitude of queries simultaneously on your behalf. This allows Search to dive deeper into the web than a traditional Google search." (source: Google blog)

Each sub-query is processed individually and in parallel based on search history and the user's account (if they have given their consent), then the results are aggregated to provide a complete and coherent response to the user, exploring various sources like the Knowledge Graph, real-time data, and information on billions of products.

Concrete Example: E-commerce Query Analysis

To illustrate how query fan-out works, let's take a real query example: "Could you suggest Bluetooth headphones with a comfortable over-ear design and long battery life?"

Google's AI Mode recognizes several facets in this query:

• Design: over-ear, comfortable
• Technology: Bluetooth
• Performance: long battery life

The system then automatically generates parallel sub-queries:

This approach allows Google to provide a comprehensive response including product recommendations, technical specifications, expert and user reviews, as well as direct purchase options.

Detailed Operation of Query Fan-Out

On ChatGPT's Side: Multi-Layer Fan-Out Inspired by Google

Recent work by RESONEO (October 2025) revealed that ChatGPT doesn't use a single search flow, but several parallel fan-outs depending on the prompt's nature. In practice, three layers coexist:

• Search Fan-out: 1 to multiple queries intended to enrich the textual response (the number of queries generated seems to depend on the LLM's evaluation of the original prompt's complexity);
• Shopping Fan-out: 1 to 3 short queries, product and price-oriented, connected to Google Shopping Graph;
• Images Fan-out: up to 10 calls to illustrate the response.

These flows are not all triggered systematically. Most queries activate two layers (Search + Shopping or Search + Images). This architecture reveals strong hybridization between ChatGPT and Google's search infrastructures (is this consented by Google? It's far from certain), notably via tokens referring to Google Shopping IDs.

Additionally, ChatGPT exploits an internal entity recognition system (NER) with proprietary taxonomy: classic entities (people, organizations, places) and product entities. This is a strategic signal for SEO experts: being identified as a relevant entity by ChatGPT increases chances of being cited in its future conversational responses.

Download the extension developed by RESONEO that allows capturing all fan-outs generated by ChatGPT after typing your prompt, and much more.

Impacts on SEO: An Evolution to Anticipate

For digital marketing and SEO professionals, query fan-out and AI Mode represent a change that must be anticipated. Here are some consequences to consider:

Content Visibility

With AI Mode, responses are generated by AI and presented directly to the user, potentially reducing traffic to websites, even though Google, through its CEO Sundar Pichai, claims otherwise. Being cited by AIs becomes the new click.

Intent Optimization

We've been telling you this for a long time, but I can't help but add another layer: creating content that targets keywords is no longer enough. You must understand and respond to the intents behind queries, that is, the entire set of user needs. This involves covering all aspects of a topic and anticipating related questions the user might ask (spoiler: AI does this very well).

Content Structure

Well-structured content, with clear subtitles, bullet lists and summaries (at the top of the page), facilitates AI analysis and increases chances of being included in generated responses.

How to Adapt Your SEO Strategy?

To remain relevant in this new environment, here are some recommendations:

Develop Comprehensive Topical Authority

Instead of targeting individual pages, create content clusters covering all aspects of a topic. Each cluster should address the sub-questions that AI might generate via query fan-out.

Adopt a "Answer Every Facet" Mentality

For any broad topic you target, identify the sub-questions or angles that users might explore and provide in-depth answers for each sub-theme.

Use Structured Data

Integrate Schema.org tags to help AI understand your page content and facilitate its inclusion in synthesized responses.

Optimize for Semantic Analysis

Structure your content with clear titles, bullet lists, summaries, short paragraphs and concise answers to common questions to facilitate AI analysis.

Monitor and Measure Differently

Use tools like Google Search Console to track AI Mode's impact on your traffic, but adapt your metrics to this new conversational search environment.

Tools and Techniques to Simulate Query Fan-Out

Besides the Chrome plugin developed by RESONEO mentioned above, several SEO tools can help you implement these strategies:

• AlsoAsked: Allows understanding your users with live "People Also Asked" data and intent clustering by Google.
• Keyword Insights: Includes keyword discovery functionality with live data from Google Autocomplete, Reddit and People Also Ask, plus search intent classification.
• Waikay: Helps identify thematic gaps in your content compared to competitors.
• InLinks: Entity-based analysis of your site, structured data implementation and internal linking.

I have also developed a Query Fan Out GPT offering the possibility, from an initial query or URL, to generate multiple queries classified by theme, search intent and order of importance, aiming to simulate the decomposition functionality of the initial query to cover a topic in its smallest details.

Andrea Volponi's Approach, CEO of WordLift

Andrea Volponi, CEO of WordLift and recognized expert in semantic AI, proposes a structured approach to adapt to query fan-out. According to him, companies must build internal "knowledge graphs" that reflect how Google's AI breaks down queries.

Volponi notably recommends using Python scripts to analyze SERPs and identify query decomposition patterns, allowing SEOs to predict which sub-queries AI Mode might generate.

Query Fan-Out: A Universal Search Model for Generative AIs

Query fan-out is not reserved for Google: it is a universal search model, adopted by most generative AIs connected to the web to combine multiple sources (textual, products, visual) in parallel to better respond to the user, with as few hallucinations as possible.

It has never been more important for digital marketing professionals who want to increase their visibility in AI engines to produce multimodal, high-quality content that responds precisely to all the needs that Internet users have, at all stages of the user journey.