Query fanouts

A fanout is a sub-question an AI engine answered on its way to answering yours. Ask an engine “best CRM for a 10-person SaaS startup” and it doesn’t answer one question. It quietly answers four or five. Which CRMs target startups. What features matter at ten seats. What the price ceiling is. How they fit a dev stack. Each of those pulls its own evidence, and the final answer is stitched together from all of them. Query Fanouts shows you that breakdown, one prompt and one engine at a time.

​

Why do fanouts matter?

Visibility Score tells you a prompt didn’t name you. Fanouts tell you what the engine was actually thinking about instead. Say you track “best CRM for startups” and never appear. The fanouts show the engine spent that answer on pricing tiers, migration effort, and free-plan limits. If your site talks about pipeline automation and nothing else, you’re not losing the race. You’re in a different one. The fix is a page that answers the sub-question, not a louder version of what you already published. That makes each sub-question a small content brief. It’s the closest thing in the product to the engine telling you what it wanted to read.

​

How does FixAEO find the sub-questions?

Engines don’t publish their internal steps, so FixAEO reads the finished answer back. A second model receives three things: your prompt, the answer text, and the list of URLs that answer cited. It’s asked to name the sub-questions the answer covered. The result is stored against the answer it came from, and refreshed whenever that answer changes. Two kinds of answer are skipped. Anything very short is too thin to break apart, and so is an answer an engine never returned — a timeout or a quota error has no sub-questions in it.

​

How fresh is the data

Fanouts are prepared in the background, so opening the page never waits on a model call. New scans are picked up on their own, and an answer that hasn’t changed isn’t re-read. A large workspace catches up over a few passes rather than all at once, so give a busy account a little time after a scan lands.

​

What’s on the page

Three counters sit at the top. Prompts is how many of your tracked prompts have fanouts in the window, and matches the number of cards below. Engines is how many distinct engines decomposed at least one of them. Sub-questions is the total across every prompt and engine pair. Below that, one card per prompt, newest scan first, ten to a page. A collapsed card shows the prompt text and the brand it belongs to. Beside those sit the engine count, the sub-question count, the date of the latest scan, and a small rail of engine logos. Open a card and, if two or more engines decomposed that prompt, you get a tab per engine tagged with its own sub-question count. A single-engine card skips the tabs and just names the engine. The list underneath is numbered in the order the reading model produced it, so number 1 is what it judged the answer led with. Switching tabs is the useful move here. Two engines rarely split the same prompt the same way, and the gap between them is often the story.

​

Filters and scope

The range tabs cover 7d, 30d, 90d and 1y, and default to 30 days. The window filters on scan date. The engine filter is a multi-select, and only appears once two or more engines have data in the window. Ticked engines are shown combined, not one at a time. Untick everything and the page honestly shows nothing. The cards are scoped to the brand you have selected in the app. See Managing brands for how the selection works. A very wide range on a busy account can return more prompts than one response holds. If that happens a banner tells you to narrow the range, and what you can see is always whole prompts rather than half a card.

​

Why is the page empty?

The empty state names both likely causes instead of picking one. It reads “either there are no tracked prompts yet, or they haven’t been scanned since you added them.” What actually populates the page is a chain of three things: There’s a second, narrower message. Once some brand in your workspace has fanouts but the selected one doesn’t, you get “no fanouts for this brand in this window” and a nudge to widen the range or switch brand. If you’ve set an engine filter, a Show all engines button appears beside that message.

​

What to do with a fanout

Read sub-questions as topics to cover. For link-level evidence — which URLs an engine actually cited and which pages it read — use Citations and Sources. Group those briefs before you write. Topics and labels shows whether a gap is one prompt or a whole theme, and Improve turns the theme into specific work.