How We Use LLM Query Fanout to Improve SEO and AEO Results

SEO is still a huge growth channel for B2B tech companies — but how people search (and how Google delivers answers) is changing fast.
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Between AI Overviews, AI Mode, featured snippets, “People also ask,” and answer-style results, the goal isn’t only ranking a page anymore. It’s also making sure your brand becomes part of the answers prospects are seeing while they research.
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That’s why we’ve been leaning into something called LLM Query Fanout.
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It’s not a buzzword. It’s a real mechanism that mirrors how modern search engines (and AI search experiences) expand questions behind the scenes.
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SEO Is Changing (Even If Rankings Still Matter)

For years, SEO was pretty straightforward:
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• Find keywords
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• Create content
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• Build authority
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• Rank pages
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• Earn clicks
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That still matters — but the layout and behavior of search results now changes the game.
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What’s different in 2026 SEO?
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Here’s what most marketing leaders are noticing:
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• More searches end without a click
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• Users get answers directly in the SERP
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• AI summaries pull information from multiple sources
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• Buying decisions happen before the “demo request” search ever occurs
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So the real question becomes:
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How do you make sure your brand is one of the sources that AI systems trust and mention?
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That’s where AEO enters the conversation.
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SEO vs AEO: What’s the Real Difference?
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Let’s keep this simple.
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Traditional SEO
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Traditional SEO is about:
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• ranking pages in the top results
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• earning clicks from Google
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• driving traffic to your site
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Answer Engine Optimization (AEO)
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AEO is about:
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• getting your content pulled into the answer itself
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• helping Google/AI confidently summarize your POV
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• showing up in AI Overviews / AI Mode / snippets / summaries
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So in practice…
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SEO = ranking
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AEO = being used as the answer
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(And yes — most good modern SEO is starting to overlap with AEO.)
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What Is LLM Query Fanout (In Plain English)?
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LLM Query Fanout is when one broad question gets expanded into many smaller related questions, so the system can return a more complete, confident answer.
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Instead of treating search like one query → one result, it becomes:
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one query → many supporting queries → combined answer
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Google has confirmed this is part of how AI search works.
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Google’s own words on Query Fanout
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Here’s a quote worth including because it’s coming directly from Google Search leadership:
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“AI Mode isn't just giving you information—it's bringing a whole new level of intelligence to search. What makes this possible is something we call our query fan-out technique."
— Elizabeth Reid, Google’s Head of Search

This matters because it validates the shift: search engines aren’t only matching keywords — they’re expanding intent.
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How LLM Query Fanout Works (Step-by-Step)
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Here’s what’s happening behind the scenes, simplified:
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1) Someone searches a broad question
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Example:
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• “Best payroll software for startups”
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2) The system expands it into sub-questions (fanout)
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It may generate related searches like:
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• “Best payroll software for 10–50 employees”
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• “Payroll software that integrates with QuickBooks”
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• “Payroll software pricing comparison”
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• “Best payroll software for remote teams”
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• “Payroll tools that support multi-state tax compliance”
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3) It gathers evidence across multiple sources
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Instead of pulling one result, it synthesizes from:
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• vendor pages
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• review sites
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• comparison posts
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• Reddit/community sources
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• authoritative blogs
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• YouTube
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• documentation
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4) It generates the answer
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This becomes:
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• AI Overview
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• AI Mode answer
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• featured snippet response
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• comparison-style SERP modules
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Why This Changes Content Strategy for B2B Tech SEO
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Here’s the part marketing leaders need to understand:
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Your audience isn’t only searching one keyword.
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They’re searching a network of questions leading to a decision.
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And AI-driven search is now building those networks automatically.
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That means your content needs “coverage,” not just keywords
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If you only publish:
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• 1 blog targeting a broad term
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…you’re often missing the dozens of supporting questions the AI system is using to form the final recommendation.
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Fanout makes this gap obvious.
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Practical SEO Examples of Using LLM Query Fanout
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This is where it becomes real (and where it becomes a differentiator for an agency).
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Example 1: Keyword research that goes deeper than “volume”
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Instead of stopping at:
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• “SOC 2 compliance checklist”
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Fanout helps expand into:
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• “SOC 2 checklist for early-stage SaaS”
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• “How long SOC 2 takes”
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• “SOC 2 vs ISO 27001”
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• “SOC 2 requirements for startups”
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• “SOC 2 automation tools”
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Result: you publish content that covers the buying journey, not just the keyword.
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Example 2: Building industry pages that actually rank and convert
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You mentioned this earlier — and yes, you’re right.
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Industry + use-case pages are becoming way more important, especially for:
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• Seed / Series A startups
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• niche categories (cybersecurity, fintech, dev tools, AI infra)
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Why?
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Because they align with intent.
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Instead of a generic page like:
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• “SEO Services”
  
  
  

You create:
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• “SEO for Cybersecurity SaaS”
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• “SEO for Fintech Startups”
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• “SEO for DevOps Tools”
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• “AEO for AI SaaS Companies”
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Fanout helps you map:
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• what each vertical cares about
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• what objections exist
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• which proof points matter
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• which subtopics Google expects you to cover
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Example 3: Making “AEO content” that AI can quote
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AEO content works best when it’s easy for an AI system to extract:
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• definitions
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• comparisons
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• step-by-step frameworks
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• short clear answers
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• structured FAQs
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Fanout helps generate a “question set” for each page so you can include:
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• FAQ sections
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• schema markup targets
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• mini-answer blocks
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• clean H2/H3 formatting
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This increases the chances your content becomes source material.
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Why This Makes Our SEO Agency Approach Different
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A lot of SEO agencies still operate with a 2018 playbook:
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• pick keywords
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• write blogs
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• add internal links
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• report rankings
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That’s not enough anymore.
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Our approach is built for modern search behavior
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We’re using query fanout thinking to:
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• structure content around intent clusters
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• build page ecosystems (not one-off blogs)
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• create content that ranks and gets referenced in AI answers
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• align SEO efforts with AEO performance
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In other words:
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We’re not trying to “hack” AI search.
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We’re aligning with the way it works.
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Final Thoughts: If AI Search Expands Queries, Your Content Should Too
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If Google is expanding one search into many sub-searches, the best SEO strategy is obvious:
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Build content that covers the entire question map.
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That’s what LLM Query Fanout helps us do — and that’s why we’re building SEO strategies that support both:
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• traditional rankings
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• AI answers / AEO visibility
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If you want help building an SEO + AEO strategy using this approach, feel free to reach out.
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FAQ Section
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What is LLM Query Fanout?
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LLM Query Fanout is the process of expanding one search query into multiple related sub-queries in order to generate a more complete and accurate answer.
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Is Query Fanout used by Google?
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Yes. Google’s Head of Search Elizabeth Reid has referenced query fan-out as part of what makes AI Mode possible.
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How does Query Fanout help SEO?
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It improves SEO by helping content teams map and create content around the broader set of questions users ask during research, which increases topical authority and ranking potential.
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How does Query Fanout help AEO?
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It helps AEO by producing content that directly answers the expanded sub-questions AI search systems rely on to create summaries and recommendations.
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