When someone asks an AI for the best software in your space, the model quietly searches dozens of related terms before answering. AI Query Fan-out reveals those hidden keywords every week, so you can write content that attracts new visitors before your competitors do.


When a user asks an AI for the best software in your market, the model does not answer from a single query. It silently fans out into several related searches, like reviews, comparisons or year-specific picks, before it picks its sources.
Without visibility into that process, you keep guessing which angle to cover. The AI Query Fan-out feature opens that black box every week.
Sorank sends your tracked prompts to ChatGPT, Claude, Perplexity, Gemini and Grok every week and records exactly which sub-queries each model explores to build its answer.
Each fan-out keyword is logged per model, so you see not just that a query exists, but which AI relies on it most. This turns a one-off snapshot into a recurring dataset you can act on.


Every tracked prompt expands into dozens of related queries, each one counted and broken down by model. You see immediately which fan-out keywords carry the most weight in your niche.
These keywords feed directly into your Topical Cluster Generator, so the opportunities you discover here turn into structured content instead of staying on a list.
Sorank queries ChatGPT, Claude, Perplexity, Gemini and Grok weekly with the prompts that matter to your market.
Each sub-query the AI explores is logged and counted, model by model.
Fan-out keywords flow into your Topical Cluster Generator, ready to become content.
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A fan-out keyword is one of the sub-queries an AI model searches internally before answering a broader question. For example, a request for the best SEO software can expand into separate searches like reviews, year-specific comparisons or pricing pages. Sorank captures these sub-queries every week across ChatGPT, Claude, Perplexity, Gemini and Grok. This gives you visibility into a process that is normally invisible. It shows you exactly what AI models look for before they pick their sources.
Sorank sends your tracked prompts to every major AI model on a weekly basis. Each run captures the fan-out keywords that came up that week, model by model. This regular cadence means you see new keywords as they appear instead of relying on a single snapshot. Over time, you build a dataset of recurring and emerging sub-queries. This makes your content decisions based on current AI behaviour, not outdated assumptions.
The fastest path is to send them into your Topical Cluster Generator, where they get grouped with related terms and checked for cannibalization. From there, you can turn the strongest ones into articles right away. Because these keywords come from real AI search behaviour, they tend to convert into citations faster than generic keyword guesses. Tracking them weekly also lets you catch new fan-out patterns before your competitors notice them. This keeps your content pipeline aligned with how AI actually searches.
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