This is an incredibly niche tool I made to help reverse-engineer how to appear in AI Overviews and LLM responses. People are increasingly turning to LLMs when making large purchasing decisions. Analyzing for consensus and summarization are some key strengths of LLMs, so this makes sense. - What are the best smart watches for runners? - What are some alternatives to Stripe that are easy to integrate? These are pretty simple prompt examples, but they help illustrate the point. A new smartwatch company making products for runners might be frustrated that LLMs or AI Overviews don't even mention them. The good news is, it's not a mystery. When someone submits a query about the top smart watches, a bunch of sub-queries are made behind the scenes to see what URLs on an actual index have to say. These subqueries are actual searches like: - "top gps smart watches 2026" - "most accurate running watches" - "garmin vs apple watch comparison" If your content is not ranking in those sub-queries, the likelihood of you being featured in the response or summary is very low. It doesn't need to be your published content, but at the very least, you need to be showing up in those results. This tool reveals the query fan-out terms for your prompt (which is not particularly novel), but it also gives you some insight into whether you should invest the time to optimize for that sub-query. It fetches and weighs: - If that term has its own AI Overview - If that term has meaningful historical search volume - If that term is particularly competitive - If that term has paid search demand It also labels the term's perceived intent and the content style most frequently cited. This is all analysis you can do on your own while jumping between a few tools, but I think it's satisfying to see at a glance.

queryfanout.io

Get insights on what LLM's search before answering.

Get insights on what LLM's search before answering.