Fan-Out Query Competitive Intelligence: See What AI Models Search Before They Answer

What are fan-out queries?

When a user asks ChatGPT, Perplexity, or Google AI a question, the AI model does not always answer from memory. It frequently performs background web searches — sometimes called “fan-out queries” — to gather fresh information before generating a response. These searches are invisible to the end user, but they determine which websites get cited and which brands appear in the answer.

Superlines captures these fan-out queries and shows you exactly:

What the AI searched for — the actual queries it sent to search engines
Which results it found — the URLs that appeared in those searches
Which pages earned citations — the specific URLs that made it into the final AI response
How your competitors rank — where competitor pages appear in the AI’s research process

This data is one of the most powerful competitive intelligence signals available in AI search optimization.

Why fan-out data changes the game

Traditional SEO competitive intelligence tells you who ranks for which keywords. Fan-out query data tells you something far more actionable: which content AI models actually trust and cite when answering questions about your market.

What fan-out data reveals

Insight	What it means	Strategic action
Competitor pages cited repeatedly	AI models trust this content as authoritative	Analyze what makes these pages citation-worthy, then create better content
Your pages appearing in searches but not cited	AI finds you but doesn’t trust you enough to cite	Audit the page for structure, authority signals, and content gaps
Topics you’re completely absent from	AI doesn’t even find you when researching these areas	Create new content targeting these specific topics
Fan-out queries that differ from the original prompt	AI rephrases and expands the user’s question	Discover adjacent topics and long-tail opportunities
Source diversity patterns	How many different domains AI pulls from	Understand whether your market is concentrated or fragmented

Getting fan-out query data

In the Superlines UI

Fan-out query data is available at the prompt level in your Superlines dashboard. Navigate to any tracked prompt and look for the fan-out analysis section, which shows the background searches, source URLs, and citation patterns.

Via MCP

The get_fanout_query_insights tool provides programmatic access to fan-out data. In Claude Desktop or Cursor:

Show me the fan-out query insights for [Brand Name]. I want to see what 
AI models search for when answering our tracked prompts, which competitor 
pages appear most often, and where we're missing from the results.

The tool returns structured data including the fan-out queries themselves, the URLs found, citation frequency, and competitive positioning.

Workflow 1: Competitive content gap analysis

This workflow uses fan-out data to identify content you need to create or improve.

Step 1: Identify your highest-priority prompts

Start with prompts where you have low visibility but high strategic importance:

For [Brand Name], show me prompts where our brand visibility is below 30% 
but at least 2 competitors are being cited. Rank by how many competitors 
appear in the results.

Step 2: Analyze fan-out queries for those prompts

For each underperforming prompt, dig into what AI models actually search for:

For the prompt "[specific prompt]", show me the complete fan-out query 
analysis:
1. What background searches does the AI perform?
2. Which URLs appear in those searches?
3. Which URLs earn citations in the final answer?
4. Are any of our pages found but not cited?

Step 3: Audit the winning pages

Once you know which competitor pages are earning citations, analyze why:

Audit these competitor URLs that are winning citations for 
"[specific prompt]":

[paste competitor URLs from fan-out data]

For each URL, analyze:
- Content structure and depth
- Schema.org markup
- Authority signals (backlinks, domain authority)
- What specific information the page provides that we don't
- Why AI models might prefer this page over ours

If you have a SERP scraper MCP (like Bright Data) connected, the agent can crawl these pages and provide a detailed content comparison automatically.

Step 4: Create your content action plan

Based on the fan-out query analysis and competitor audit for 
"[specific prompt]", create a content action plan:

1. What new pages should we create?
2. What existing pages should we update?
3. What specific information, structure, or markup changes would 
   make our content more citation-worthy?
4. What related topics from the fan-out queries should we cover?

Workflow 2: Steal competitive content ideas

Fan-out data reveals not just who’s winning, but exactly what content earns AI trust. This workflow systematically extracts content ideas from competitor wins.

The competitive citation audit

For [Brand Name], run a competitive citation audit:

1. Across all our tracked prompts, which competitor URLs are cited 
   most frequently?
2. Group these URLs by topic/theme
3. For the top 10 most-cited competitor pages, explain what makes 
   them citation-worthy
4. For each one, suggest a content piece we could create that would 
   compete for the same citations

Cross-prompt pattern analysis

The same competitor page might be cited across many different prompts. This reveals their strongest content assets:

Show me competitor domains that appear in fan-out results across 5 or 
more of our tracked prompts. For each domain:
1. Which of their pages appear most often?
2. Which prompts do they dominate?
3. What content format do they use (blog posts, guides, tools, data)?
4. What's our equivalent content, if any?

Workflow 3: Discover content topics from AI search behavior

Fan-out queries often reveal topics you haven’t considered. When an AI model receives the prompt “What’s the best project management tool for startups?”, it might search for “project management tool comparison 2026”, “startup project management needs”, and “Trello vs Asana vs Monday for small teams.” Each of these searches represents a topic opportunity.

Topic discovery prompt

Analyze fan-out queries across all our tracked prompts for [Brand Name]. 
Find fan-out search queries that represent topics we don't currently have 
content for:

1. List unique topic clusters from fan-out queries
2. Mark which topics we already cover (based on our cited URLs)
3. Highlight topics where competitors have content but we don't
4. Estimate priority based on how many prompts trigger each topic

From topics to content briefs

For the top 5 uncovered topics from the fan-out analysis, create content 
briefs:

For each topic:
- Suggested title and format (article, guide, comparison, tool)
- Key questions to answer (based on the fan-out queries that surface this topic)
- Competitor content to reference and improve upon
- Target prompts this content should help us win
- Schema.org markup recommendations

Workflow 4: Automated competitive monitoring

Fan-out competitive intelligence becomes most powerful when automated. This workflow sets up continuous monitoring.

Weekly competitive snapshot

Run this prompt weekly (or set up as part of an automated pipeline):

Generate a weekly fan-out competitive intelligence report for [Brand Name]:

1. NEW THREATS: Competitor URLs that appeared in fan-out results this week 
   but weren't there last week
2. LOST GROUND: Prompts where we were previously cited but competitor 
   content has displaced us
3. OPPORTUNITIES: Fan-out queries where no strong content exists yet 
   (fragmented sources, low-quality results)
4. WINS: Prompts where our citations increased this week

Format as a structured report with priority actions.

Automated alert workflow

For teams building agentic workflows, you can create an autonomous monitoring system:

Check fan-out query data for [Brand Name] and compare with last week's 
baseline:

1. If any new competitor URL is cited in 3+ of our tracked prompts, 
   flag as HIGH PRIORITY and analyze the page
2. If we lost citations on any strategic prompt (labeled "strategic"), 
   generate an immediate action plan
3. If new fan-out query topics emerge that we don't track, add them 
   as suggested prompts with the label "fanout-discovery"

Save the report to our workspace for review.

Workflow 5: Feed fan-out intelligence into content pipelines

The highest-leverage use of fan-out data is feeding it directly into content creation workflows.

The intelligence-to-content loop

Fan-Out Intelligence                Content Pipeline
                                    
1. Identify prompts where           4. Create content briefs
   we underperform                     informed by fan-out data
        │                                    │
        ▼                                    ▼
2. Analyze fan-out queries           5. Write content that
   to find what AI searches             addresses the exact topics
   for and who wins                      AI models search for
        │                                    │
        ▼                                    ▼
3. Audit top-performing              6. Publish, then track
   competitor content                    citation changes in
                                         Superlines
                                              │
                                              ▼
                                    7. Repeat — new fan-out
                                       data shows impact

Connecting to a content agent

If you’re using the Agentic AEO Content Pipeline, fan-out data is already integrated into Phase 1 (Intelligence Gathering) and Phase 2 (Competitive Deep Dive). The agent uses get_fanout_query_insights to discover what AI models search for, then analyze_competitor_url with Bright Data scraping to understand why certain pages win.

For simpler setups, you can manually chain the intelligence into content creation:

Using the fan-out query analysis from our top 5 underperforming prompts:

1. Identify the 3 highest-priority content pieces we should create
2. For each piece, write a detailed content brief that:
   - Covers the specific topics AI models search for (from fan-out data)
   - Matches or exceeds the depth of the top-cited competitor content
   - Includes recommended Schema.org markup
   - Targets specific prompts and fan-out queries
3. If possible, draft the first article in full

Advanced: Combining fan-out data with prompt discovery

Fan-out queries are also a discovery mechanism for new prompts to track. The background searches AI performs often reveal adjacent questions and topics that your audience cares about.

Fan-out to prompt pipeline

Analyze all fan-out queries from our tracked prompts for [Brand Name]:

1. Extract unique fan-out search queries that look like questions a 
   user might directly ask an AI assistant
2. Check which of these we're already tracking as prompts
3. For any that we're NOT tracking, estimate their strategic importance 
   based on:
   - How many of our tracked prompts trigger this fan-out query
   - Whether competitors are well-positioned for these topics
   - Whether we have existing content that could compete
4. Add the top 10 most important ones to Superlines tracking with the 
   label "fanout-discovered"

This creates a self-reinforcing loop: your tracked prompts generate fan-out data, which reveals new prompts to track, which generates more fan-out data. Over time, your prompt portfolio grows organically to cover the full landscape of how AI models research your market.

Key takeaways

Fan-out queries are the X-ray of AI search — they show the research process AI models use before generating answers
Competitive intelligence from fan-out data is more actionable than traditional SEO data — it shows which content AI models actually trust and cite
Content gaps are immediately visible — if your pages appear in fan-out searches but aren’t cited, you know exactly what to fix
Automation multiplies the value — weekly monitoring and automated alerts turn fan-out intelligence into a continuous competitive advantage
The data feeds content strategy directly — fan-out topics tell you exactly what to write about to earn more citations

Start with Workflow 1 to identify your most urgent competitive gaps, then build toward automated monitoring as you develop your agentic workflow capabilities.