1. What Query Fan-Out reveals about AI search
When you ask AI modelsl ike ChatGPT or Perplexity a question, the model doesn't just answer from memory. It expands your question into multiple search queries, runs them, and synthesises the results into one response. Those behind-the-scenes queries are called query fan-out.
ALLMO's Query Fan-Out page surfaces them: the actual search queries AI models issued while answering the prompts in your report, shown as a word cloud and as a searchable table.
2. Why fan-out queries matter more than user prompts
Traditional keyword research tells you what people type into Google. Query fan-out tells you what the AI types on their behalf, and those are the queries that actually decide which pages get read and cited.
This matters because:
The AI's queries are often more specific than the user's prompt. A prompt like "best CRM for startups" might fan out into "CRM free tier startup", "CRM with HubSpot integration", "CRM pricing under 50 dollars". You need to rank for the fan-out, not just the prompt.
Fan-out queries reveal the sub-topics the AI considers relevant. If the AI keeps issuing queries about pricing, integrations, or security, those are the topics your content must cover to be retrieved.
They expose new keyword opportunities that wouldn't show up in any Google-focused keyword tool.
3. How ALLMO captures and aggregates AI search queries
Every time ALLMO runs a prompt against a search-enabled model, the fan-out queries are captured alongside the response.
Queries are normalised (lowercased, deduplicated) and aggregated across your report.
The word cloud shows the most common words across all fan-out queries.
The table groups by normalised query text and counts frequency, model mix, and last-seen date.
Filters (model, date, language, country, tags) are applied to prompts first, then to the resulting responses, then to the query rows.
Clicking any query opens a drill-down showing which prompts triggered it and when, with links back to Prompt Analytics for full context.
4. How to explore the fan-out word cloud and query table
Open Query Fan-Out inside the Explore section via the sidebar.
Start with the word cloud to spot dominant themes at a glance. Click any word to filter the table.
Use the search bar for specific terms you want to investigate.
Sort the table by frequency to see which fan-out queries are most common.
Check the model column to see whether a query is issued across all models or specific to one.
Click any row to open the drill-down: which prompts triggered it, on what dates, from which responses.
From the drill-down, jump into Prompt Analytics to see how the response actually used the results.
5. How to turn fan-out queries into a content roadmap
You see a high-frequency query you don't have content for: That's a content brief writing itself. Create a page that directly answers that query. These are low-competition opportunities because most competitors are optimising for the user-facing prompt, not the underlying fan-out.
Your product name appears in fan-out queries: Models are actively looking for information about you. Make sure your site ranks for branded + modifier queries ("yourbrand pricing", "yourbrand vs competitor", "yourbrand reviews").
A competitor's name appears in fan-out queries linked to your prompts: Models consider them a reference point in your category. You need comparison content, ideally on your own domain, that directly addresses the comparison.
Fan-out queries cluster around a feature you have but don't market well: This is a positioning signal. If the AI keeps searching for "integration with X" and you have that integration, make it prominent on your site.
Fan-out queries reveal a pain point or use case you hadn't considered: The AI is telling you what your audience actually cares about, filtered through thousands of conversations. Take the signal seriously.
A query shows up across many prompts: It's a hub query. Ranking for it provides compounding value because it surfaces in many different user conversations.
Check how semantically similar each fan-out query is to the original prompt. A high similarity means the model is staying close to what the user asked, so ranking for the prompt itself is enough. Low similarity means the model is reformulating heavily into adjacent sub-topics, and you'll only get cited if your content also covers those reformulations, not just the original phrasing.
Query Fan-Out is one of the most underused feature in most GEO workflows. Review it regularly, to build a content roadmap grounded in how AI actually searches.