Summary
- Query fan-out reveals how AI models branch a single user prompt into multiple sub-queries, cross-checking data across sources before generating a response -- understanding this is critical for building content that ranks in AI search
- Prompt difficulty scoring helps you prioritize which prompts to target by showing competition levels and likelihood of citation -- essential for resource-constrained teams
- Most GEO tools only track citations after the fact; the platforms reviewed here help you identify gaps and optimize proactively
- Promptwatch leads the category with both query fan-out visualization and prompt difficulty metrics, plus content gap analysis and an AI writing agent to close those gaps
- Otterly.AI offers free query fan-out generation but lacks difficulty scoring and optimization features
What is query fan-out and why does it matter?
When you ask ChatGPT "What's the best CRM for small businesses?", the model doesn't just pull one answer from one source. Behind the scenes, it fans out into multiple sub-queries:
- "CRM features small businesses need"
- "CRM pricing comparison"
- "CRM ease of use reviews"
- "CRM integrations for startups"
- "HubSpot vs Salesforce for SMBs"
Each sub-query gets researched independently. The model cross-checks data from multiple sources, synthesizes the findings, then generates a single coherent response. If your content only answers the top-level question but misses the sub-queries, you're invisible in the final answer.
This is why traditional SEO keyword research falls short for AI search. You can't just optimize for "best CRM for small businesses" and call it done. You need to map the entire fan-out tree -- every angle the AI model explores when answering that prompt.
Query fan-out analysis shows you which sub-queries your content is missing. It reveals the specific topics, angles, and questions AI models want answers to but can't find on your site. Most brands discover they're only covering 20-30% of the fan-out tree for their target prompts.
What is prompt difficulty scoring?
Prompt difficulty scoring works like keyword difficulty in traditional SEO, but for AI search. It estimates how hard it will be to get cited for a specific prompt based on:
- How many authoritative sources already rank for related queries
- The quality and depth of existing content in the citation pool
- How frequently the prompt is being asked (volume)
- How competitive the topic is (number of brands targeting it)
A difficulty score helps you prioritize. If you're a startup with limited resources, you don't want to waste time creating content for ultra-competitive prompts where established players already dominate. You want to find the gaps -- high-value prompts with manageable difficulty where you can actually win citations.
Most GEO tools show you where you're already being cited. Prompt difficulty scoring shows you where you could be cited if you create the right content. That's the difference between monitoring and optimization.
Why most GEO tools don't offer these features
The majority of GEO platforms are monitoring-only dashboards. They track:
- Which prompts mention your brand
- How often you're cited
- Sentiment of those citations
- Competitor benchmarking
That's valuable data, but it's reactive. You see what's already happening, not what you should do next.
Query fan-out and prompt difficulty scoring require a different architecture. The platform needs to:
- Simulate how AI models actually process prompts (not just track final outputs)
- Analyze billions of citations to understand competitive landscapes
- Map semantic relationships between prompts and sub-queries
- Estimate volume and difficulty based on real usage data
This is computationally expensive and requires proprietary datasets that most tools don't have. That's why only a handful of platforms offer these features -- and why they're worth paying attention to.
The platforms that actually offer query fan-out and difficulty scoring
Promptwatch: The only platform with both features plus content optimization
Promptwatch is the only GEO platform that combines query fan-out visualization, prompt difficulty scoring, and content optimization tools in a single workflow.
Here's how it works:
Answer Gap Analysis shows you which prompts competitors are visible for but you're not. For each gap, you see:
- The full query fan-out tree (every sub-query the AI model explores)
- Difficulty score (how hard it is to rank)
- Estimated volume (how often the prompt is asked)
- Which competitors are currently cited
- Which specific content angles are missing from your site
This isn't generic keyword research. It's based on 880M+ citations analyzed across ChatGPT, Claude, Perplexity, Gemini, Google AI Overviews, and other models. You see exactly what AI engines want to cite but can't find on your site.
The built-in AI writing agent then generates articles, listicles, and comparisons grounded in that citation data. It's not generic SEO filler -- it's content engineered to answer the specific sub-queries in the fan-out tree, using the depth and structure AI models prefer.
Page-level tracking closes the loop. After you publish, you see which pages are being cited, how often, and by which models. Traffic attribution (code snippet, GSC integration, or server log analysis) connects visibility to actual revenue.
This is the action loop most competitors lack: find gaps → generate content → track results. Most tools stop at step one.
Additional capabilities:
- AI Crawler Logs: Real-time logs of ChatGPT, Claude, Perplexity crawlers hitting your site
- Citation & Source Analysis: See which pages, Reddit threads, YouTube videos AI models cite
- Reddit & YouTube Insights: Surface discussions that influence AI recommendations
- ChatGPT Shopping Tracking: Monitor product recommendation visibility
- Competitor Heatmaps: Compare your visibility vs competitors across LLMs
- Multi-language & Multi-region: Track AI responses in any language, from any country
Pricing: Essential $99/mo (1 site, 50 prompts, 5 articles), Professional $249/mo (2 sites, 150 prompts, 15 articles, crawler logs), Business $579/mo (5 sites, 350 prompts, 30 articles). Free trial available.
Who it's for: Marketing teams, SEO teams, digital agencies, and any brand that wants to be visible in AI search results.
Otterly.AI: Free query fan-out generation, but no difficulty scoring
Otterly.AI offers a free query fan-out generator that shows how a single prompt branches into sub-queries. You input a prompt, select the AI model (ChatGPT, Perplexity, etc.), and it visualizes the fan-out tree.
This is useful for understanding how AI models think, but it's a standalone feature. Otterly doesn't provide:
- Difficulty scores for those sub-queries
- Volume estimates
- Content gap analysis (which sub-queries you're missing)
- Optimization tools to actually create content that ranks
It's a monitoring-only platform. You see where you're cited, track sentiment, and benchmark competitors. The fan-out generator is a nice addition, but it doesn't integrate with the rest of the workflow.
Pricing: $25/mo for basic monitoring. Fan-out generator is free.
Who it's for: Brands on a tight budget who want basic citation tracking and occasional fan-out analysis.
Profound: Enterprise-grade monitoring with limited fan-out insights
Profound is an enterprise GEO platform with deep analytics, compliance (SOC 2 Type II, HIPAA), and dedicated support. It tracks citations across 10+ AI models and provides competitor benchmarking.
Profound offers some fan-out insights -- you can see related prompts and sub-queries that connect to your target prompts -- but it's not a full visualization like Promptwatch or Otterly. There's no difficulty scoring, no volume estimates, and no content generation tools.
What Profound does well: compliance, white-glove support, and granular analytics for Fortune 500 brands. If you need SOC 2 or HIPAA compliance, it's one of the few options. But for query fan-out and difficulty scoring specifically, it's limited.
Pricing: From $499/mo. Custom enterprise pricing available.
Who it's for: Fortune 500 enterprises with compliance requirements and large budgets.
AthenaHQ: Prompt volume tracking, but no fan-out or difficulty
AthenaHQ tracks prompt volume -- how often specific prompts are being asked across AI models. This is useful for prioritization, but it's not the same as difficulty scoring. Volume tells you demand; difficulty tells you competition.
AthenaHQ doesn't offer query fan-out visualization or content gap analysis. It's a monitoring platform with ecommerce integrations (Shopify) and a GEO score that aggregates your overall visibility.
If you're an ecommerce brand and want to track product-related prompts, AthenaHQ is worth considering. But for fan-out and difficulty specifically, it's not built for that.
Pricing: From $295/mo.
Who it's for: Ecommerce brands tracking product visibility in AI search.
Semrush AI Toolkit: Fixed prompts, no fan-out, no difficulty
Semrush added an AI visibility toolkit in 2026, but it uses a fixed set of prompts. You don't get custom prompt tracking, fan-out analysis, or difficulty scoring. It's a bolt-on feature for existing Semrush users, not a purpose-built GEO platform.
If you're already paying for Semrush and want basic AI visibility tracking, it's a convenient add-on. But it's not competitive with dedicated GEO tools for fan-out and difficulty.
Pricing: From $99/mo (requires existing Semrush subscription).
Who it's for: Semrush users who want basic AI visibility tracking without switching platforms.
Ahrefs Brand Radar: Fixed prompts, no fan-out, no difficulty
Ahrefs Brand Radar tracks brand mentions in AI search results, but like Semrush, it uses a fixed set of prompts. No custom tracking, no fan-out, no difficulty scoring.
It's in beta (free for now), so it's worth testing if you're already an Ahrefs user. But it's not a full GEO platform.
Pricing: Beta free, pricing TBD.
Who it's for: Ahrefs users who want to monitor brand mentions in AI search.
How to use query fan-out and difficulty scoring in your GEO strategy
Here's the workflow:
1. Identify your target prompts
Start with the prompts your ideal customers are asking. Not SEO keywords -- actual questions people type into ChatGPT, Perplexity, or Google AI.
Examples:
- "Best project management software for remote teams"
- "How to reduce churn in SaaS"
- "What's the difference between CDP and CRM?"
Use a tool like Promptwatch to see which prompts have volume and are relevant to your business.
2. Analyze the fan-out tree
For each target prompt, map the sub-queries. What angles is the AI model exploring? What questions does it need answered to generate a complete response?
Example fan-out for "Best project management software for remote teams":
- "Project management features for remote work"
- "Asynchronous communication tools"
- "Time zone management in project software"
- "Integration with Slack, Zoom, etc."
- "Pricing comparison for remote teams"
- "User reviews from distributed teams"
If your content only covers the top-level prompt but misses these sub-queries, you're not getting cited.
3. Check difficulty scores
Not all prompts are worth targeting. If difficulty is 90/100 and you're a startup, you're probably wasting time. Look for prompts with:
- Manageable difficulty (30-60 range)
- Decent volume (being asked regularly)
- High relevance to your business
This is where prompt difficulty scoring saves you months of wasted effort.
4. Create content that covers the full fan-out
Don't just write a generic "Best X" listicle. Create comprehensive content that answers every sub-query in the fan-out tree.
For the project management example above, that means:
- A section on async communication features
- A comparison table with pricing
- User reviews and case studies from remote teams
- Integration details
- Time zone management tips
AI models cite content that's comprehensive and structured. They skip surface-level articles.
5. Track results and iterate
After publishing, monitor:
- Which pages are being cited
- How often
- By which AI models
- What traffic and conversions result
If you're not getting cited, go back to the fan-out tree. Which sub-queries are you still missing? What depth or structure is lacking?
This is an iterative process. Most brands see results within 2-4 weeks of publishing optimized content.
Comparison table: Query fan-out and difficulty scoring features
| Platform | Query Fan-Out | Difficulty Scoring | Volume Estimates | Content Gap Analysis | AI Writing Agent | Pricing |
|---|---|---|---|---|---|---|
| Promptwatch | ✓ Full visualization | ✓ Yes | ✓ Yes | ✓ Yes | ✓ Yes | From $99/mo |
| Otterly.AI | ✓ Free generator | ✗ No | ✗ No | ✗ No | ✗ No | From $25/mo |
| Profound | ~ Limited | ✗ No | ✗ No | ✗ No | ✗ No | From $499/mo |
| AthenaHQ | ✗ No | ✗ No | ✓ Yes | ✗ No | ✗ No | From $295/mo |
| Semrush | ✗ No | ✗ No | ✗ No | ✗ No | ✗ No | From $99/mo |
| Ahrefs | ✗ No | ✗ No | ✗ No | ✗ No | ✗ No | Beta free |
Why this matters more in 2026 than ever
AI search volume is accelerating. 37% of consumers now start searches with AI instead of Google. Gartner predicts traditional search volume will drop 25% by 2026.
Brands that ignore AI search are already losing visibility. But most brands are approaching GEO the wrong way -- they're monitoring citations after the fact instead of optimizing proactively.
Query fan-out and prompt difficulty scoring flip the script. Instead of reacting to where you're already cited, you identify gaps and prioritize winnable prompts before creating content. You see exactly what's missing, then fix it.
This is the difference between a monitoring dashboard and an optimization platform. Most GEO tools are dashboards. The platforms reviewed here -- especially Promptwatch -- are optimization platforms.
Common mistakes brands make with fan-out and difficulty
Mistake 1: Targeting high-difficulty prompts first
Most brands go after the obvious, high-volume prompts ("best CRM", "top marketing tools") without checking difficulty. These prompts are dominated by established players with massive content libraries. You're not going to outrank them in AI search without years of investment.
Start with mid-difficulty prompts where you have a realistic shot at citations.
Mistake 2: Ignoring the fan-out tree
You write a 2,000-word article on "best project management software" and wonder why AI models aren't citing it. The reason: you only answered the top-level question. You didn't cover the 15 sub-queries in the fan-out tree.
AI models cite comprehensive content that answers every angle. Surface-level articles get skipped.
Mistake 3: Not tracking which sub-queries drive citations
You publish content covering the full fan-out, but you don't track which sub-queries are actually driving citations. Maybe the "pricing comparison" section is getting cited heavily, but the "integration details" section is ignored.
Page-level tracking shows you which content is working. Double down on what's getting cited, cut what's not.
Mistake 4: Using generic SEO keyword research
SEO keyword tools show search volume for Google. They don't show:
- How often prompts are asked in ChatGPT or Perplexity
- How AI models fan out those prompts into sub-queries
- Which competitors are being cited
- What content angles are missing
AI search is a different game. You need AI-native research tools.
The future of query fan-out and difficulty scoring
Right now, only a handful of platforms offer these features. That will change as more brands realize monitoring-only tools aren't enough.
Expect to see:
- More platforms adding fan-out visualization (but most will be shallow implementations)
- Difficulty scoring becoming standard (like keyword difficulty in SEO tools)
- Integration with AI writing agents (the platforms that combine research + generation will win)
- Real-time fan-out tracking (as AI models update their query patterns)
The platforms that move fastest on this will dominate the GEO category. Right now, Promptwatch is the only one with the full stack: fan-out, difficulty, content gaps, and AI generation. That's a 12-18 month lead over competitors.
How to get started
If you're serious about AI search visibility, here's the action plan:
-
Audit your current visibility: Use a tool like Promptwatch to see where you're already being cited (or not cited) across AI models.
-
Identify content gaps: Run an Answer Gap Analysis to see which prompts competitors are visible for but you're not. Look at the fan-out trees and difficulty scores.
-
Prioritize winnable prompts: Focus on mid-difficulty prompts with decent volume and high relevance to your business. Don't waste time on ultra-competitive prompts.
-
Create comprehensive content: Cover the full fan-out tree, not just the top-level prompt. Use an AI writing agent (like Promptwatch's) to generate content grounded in citation data.
-
Track and iterate: Monitor which pages are being cited, by which models, and what traffic results. Double down on what works.
This is the workflow that actually moves the needle. Most brands skip steps 2-4 and wonder why they're not getting cited.
Final thoughts
Query fan-out and prompt difficulty scoring are the next evolution in GEO. They shift the focus from reactive monitoring to proactive optimization.
Most GEO tools show you what's already happening. The platforms reviewed here show you what should happen -- which prompts to target, which content to create, and how to prioritize your efforts.
If you're choosing a GEO platform in 2026, these features should be non-negotiable. Without them, you're flying blind.