Query Fan-Out: A Data-Driven Approach to AI Search Visibility

Google’s AI Mode search doesn’t just process your query—it explodes it into multiple sub-queries, fanning out across Google’s knowledge graphs and its web index to synthesize comprehensive answers. This “query fan-out” process represents the most significant shift in search behavior since mobile-first indexing, yet it seems as if we’re all flying blind, unable to measure how well our content performs against this new reality.

After reviewing patents like the US 2024/0289407 A1 recently brought up by Michael King on LinkedIn and studying AI search patterns for several months, I’ve developed a practical framework that not only explains what’s happening but provides the tools to measure and optimize for it. The breakthrough isn’t just understanding query fan-out—it’s being able to simulate and score it.

The Query Fan-Out Reality: Beyond Deterministic Rankings

When someone asks Google’s AI “What’s the best sustainable marketing strategy for small e-commerce businesses?”, the AI doesn’t search for that exact phrase. Instead, it decomposes the query into multiple sub-queries that vary based on user context:

• What makes a marketing strategy sustainable?
• Which marketing channels work best for e-commerce?
• What are the budget constraints for small businesses?
• How do sustainable practices impact customer acquisition?
• What are successful case studies in sustainable e-commerce marketing?

But here’s the critical insight from my work since the SGE (Search Generative Experience) days: these follow-up questions aren’t the same for everyone. They’re deeply contextual, stochastic, and impossible to predict deterministically.

The problem for marketers isn’t just optimizing for sub-queries—it’s accepting that there’s no way to “rank” or even track visibility in traditional terms. The game has fundamentally changed.

A New Framework: Predicting Questions

Traditional SEO tools can’t measure query fan-out because they assume deterministic rankings still exist. They don’t. What we need instead is a system that helps us build semantic infrastructure capable of handling the infinite variations of contextual follow-up questions.

I’ve been working on this challenge since SGE, developing what I call a “validation function“—a reward policy to ground model predictions about the next query. The methodology acknowledges the stochastic nature of the problem:

1. Entity Identification: Extract the main ontological concept from your content
2. Synthetic Query Generation: Simulate possible follow-up questions, understanding these are samples, not exhaustive lists
3. Semantic Layer Assessment: Measure how well your content’s semantic structure can adapt to variations
4. Validation Function: Develop reward policies that improve prediction accuracy for contextual follow-ups

The goal isn’t to predict every possible query—it’s to build semantic infrastructure that increases our accuracy in predicting the next likely question based on user context. Gianluca Fiorelli has been working on this for years recommending us to retrieve all the query refinements (Topic filter, PAA, People Also Search For, Images Search tag queries etc) that Google presents for a seed query.

Now, before, sharing how we’re planning to scout for follow-questions, let’s review the core principles behind the framework.

The Strategic Framework for AI-Search Optimization: 10 Core Principles

Based on testing and analysis of successful AI-visible content, these are the strategic principles that actually move the needle:

1. Focus on Ontological Core, Not Content Volume

The shift isn’t about producing more content—it’s about building a robust semantic foundation. Your ontological core (as introduced by Tony Seal) is the structured representation of essential domain knowledge. It’s what enables AI systems to generate dynamic, context-aware responses.

Since follow-up questions vary by user, you can’t prewrite content for every possibility. Instead, invest in semantic infrastructure that can adapt and scale—anchored in entities, relationships, and intent—so your content stays relevant, no matter the path the conversation takes.

2. Build Dynamic, Conversational Content Architecture

Since follow-up questions are both contextual and stochastic, your content must be conversational and adaptive—not merely static or exhaustive. Rather than chasing keyword coverage, shift your focus to semantic relationships and knowledge graph structures.

Our data shows that content built on a strong ontological foundation can respond to 3x more contextual variations than traditional long-form copy. Every entity relationship opens a new conversational path; every attribute becomes a new potential answer.

3. Prioritize E-E-A-T and Structured Data Rigorously

Google has made it clear: E-E-A-T is critical for AI-generated responses. But beyond traditional trust signals, you also need to provide explicit semantic context through schema markup.

The combination of human credibility and machine readability is now essential for maximum visibility. In an AI Agent–driven world, trust and authenticity aren’t optional—they’re foundational.

4. Adapt to Conversational and Intent-Driven Queries

AI excels at understanding natural language—your content strategy should reflect this by focusing on underlying intent, not just keyword phrases. It’s not about ranking for exact terms anymore; it’s intent and semantic similarity that win the game.

Our initial tests on SGE show that content optimized for conversational queries achieves 40% higher coverage in fan-out simulations.

5. Develop Prediction Models for Contextual Follow-ups

Universal coverage isn’t the goal. Rather than optimizing for individual queries, focus on building data—and eventually models—that can predict likely follow-up questions based on user context.

This is where the validation function becomes essential: it should reward predictions that not only anticipate the next contextual turn but also align with your ontological core—your brand values and marketing position.

The objective is to increase prediction accuracy for user-specific follow-ups, not to cover everything.

6. Excel with “Zero-Click” and “Citation-Based” Visibility

Success metrics are shifting. Being cited in AI-generated answers can be more impactful than traditional clicks—especially for brand authority and consideration. But these are still proxy metrics.

What truly matters is the bottom line: optimize for conversions and leads. That’s the North Star—everything else is secondary.

Track mention frequency and sentiment in AI responses, not just CTR. Then connect the dots: Are these mentions driving trial signups if you’re SaaS? Purchases if you’re e-commerce?

SEO is no longer just about rankings—it’s a function of business development.

7. For E-commerce, Focus on Rich Product Data and Comparative Content

AI-driven search doesn’t just answer direct queries—it supports users through complex decision-making journeys. To win in this environment, your product content must address the comparative sub-questions that AI systems generate: How does this product differ? What are the key features and use cases? Why choose this over another?

Pages enriched with structured data and comparison matrices—highlighting specifications, benefits, and alternatives—perform significantly better. Learn more on how you can convert product data (enriched with GS1 Digital Link) with additional visibility and sales.

8. Monitor Paid Search Performance and Adapt Strategies

AI Overviews are reshaping the search landscape—including paid visibility. Closely monitor CTR fluctuations for query types where AI delivers comprehensive answers, and reallocate budgets where necessary. Always remember that an effective landing page reduces the cost per click.

Prioritize high-intent queries—those where AI responses lead users to take action, not just feel informed.

9. Stay True to Your Values and Listen Strategically

As AI reshapes how we create, optimize, and discover content, staying grounded in your core values is more important than ever. Prioritize transparency, fairness, and privacy—not just for compliance, but to build enduring trust in an increasingly automated world.
At the same time, adopt a clear listening strategy: structure your data infrastructure around well-defined personas. These personas serve as the foundation for inferring the most relevant follow-up questions, guiding content generation and optimization in a way that truly resonates with your audience.

10. Experiment and Iterate Continuously

The AI search landscape evolves rapidly. Use tools like our query fan-out simulator to regularly assess your content’s AI visibility and adapt strategies based on actual coverage data.

The Validation Function: Our Next Breakthrough

Understanding query fan-out is one thing—predicting contextual follow-ups is another entirely. That’s why we’re developing a validation function alongside this tool, a reward policy that grounds model predictions about the next query based on user context.

This work began during the SGE era, recognizing that the principle remains the same: marketers need to focus on follow-up questions, but these aren’t just stochastic—they’re deeply determined by user context and the core values of every brand.

The current Colab notebook provides a foundation for approaching this problem, offering:

• Ontological entity identification from your content
• Sample follow-up generation that demonstrates the principle (not comprehensive coverage)
• Semantic infrastructure assessment showing how well your knowledge structure adapts
• Foundation for validation functions that will improve contextual prediction accuracy

The tool uses DSPy (my favorite SEO programming framework( for robust system building, acknowledging that we’re building toward dynamic, context-aware prediction rather than static optimization.

🧪 This tool is experimental. I’d love to hear your feedback and suggestions as we continue refining it together with the community.

Access the Query Fan-Out Simulator – A free tool to measure your content’s AI search visibility on Google Colab.

From Coverage to Context: The Real Challenge

The notebook reveals something more profound than coverage gaps—it shows the impossibility of deterministic optimization in an AI-driven search world. Most content addresses only samples of possible follow-ups, and that’s not a bug, it’s the feature.

What matters isn’t covering every possible query variation—it’s building semantic infrastructure that can predict and respond to contextual follow-ups accurately. When you understand that there’s no way to “rank” in the traditional sense, you start focusing on what actually matters: the ontological core.

This is why content becomes dynamic and conversational. In the end, the ontological core—your fundamental knowledge structure about your domain—is all you really need to focus on. Everything else is prediction and adaptation.

The brands that understand this shift from deterministic rankings to contextual prediction will dominate AI search. The question isn’t whether you can cover every query variation—it’s whether you can build semantic infrastructure that predicts and adapts to user context.

Ready to start building your ontological foundation? The simulator is your first step to understanding how contextual follow-ups work—and why traditional SEO thinking no longer applies.

👉 Access the Query Fan-Out Simulator — a free tool to measure your content’s AI search visibility [runs on Google Colab].

👉 Book a demo to see how we can help you transform your content strategy with knowledge graphs and AI-powered optimization.

References:

For further reading on the evolution of Google’s AI Mode and its implications for search and SEO, see:

• Gianluca Fiorelli’s “Why AI Mode Will Replace Traditional Search as Google’s Default Interface” on I Love SEO
• Aleyda Solís’s breakdown of the Query Fan-Out approach
• Dennis Goedegebuure’s perspective on SEOs as natural-born marketers in the age of AI
• Michael King’s in-depth analysis of the AI Mode patent
• The official patent document for “Search with stateful chat” (US20240289407A1)