How AI Answer Engines Pull Information: Query fan-out, entities, and sources
Someone types a simple question into an AI assistant.
“Is [Brand] a good fit for teams like ours?”
On the surface, it looks like one question. In practice, it rarely stays that clean. The assistant is trying to be useful, so it expands the ask, fills in blanks the user did not type, and builds an answer that covers the full conversation it thinks the user is having.
That’s why AI responses can feel like a stitched-together summary. They usually are.
One question turns into many
Query fan-out is the easiest way to understand what’s happening.
The prompt you see is not the only thing the system is answering. A single question tends to be broken into smaller ones, then reassembled into a single response that sounds confident and complete.
Take a common prompt like “Compare [Brand] vs [Competitor].” Behind the scenes, that can turn into a handful of sub-questions. Some are definitions. Some are fit questions. Some are comparisons. Some are objections.
You don’t need to map every branch to make this worthwhile. You just need to accept the basic reality: the model is trying to cover the whole topic, not just your exact wording.
Here’s what a simple prompt often expands into:
What is [Brand] in plain language?
What category does it belong to?
Who is it for and who is it not for?
What does it do well?
How is it different from [Competitor]?
What are the common complaints or limitations?
This also explains why answers fluctuate. You are not looking at a stable ranking page. You are looking at a synthesis. Small changes in phrasing can send the system down a slightly different branch.
So the goal is not to force one perfect output. The goal is to make sure the underlying story is sturdy enough that the answer keeps landing in the right place across variations.
Entities decide who the answer is about
Before the system can answer anything, it has to decide what you are.
Not what you want to be. What you are, as an entity.
That step gets overlooked because it feels like table stakes. But when answers are off, it’s often because the system never anchored the entity correctly in the first place. Once that happens, the category can drift, competitors can change, and old positioning can sneak back in.
You see it in a few familiar ways.
Sometimes you get flattened into the generic version of your space. Sometimes you get described using language your team stopped using a year ago. Sometimes you keep getting compared to a competitor that doesn’t show up in deals, but does show up in a lot of third-party content.
If entity clarity is the issue, the fix usually isn’t more content. It’s cleaner signals.
A quick test that works in real life: could someone who has never heard of your company land on your site and describe you correctly after reading one page?
If the answer is no, the assistant will struggle too.
This is also where the boring SEO foundations matter more than anyone wants to admit. Crawlability, duplication, and structure still affect discoverability. Even if the model is not crawling your schema directly, it’s still operating in an ecosystem where search and indexing influence what gets surfaced and repeated.
Sources are not just your website
The next thing that surprises teams is how rarely the answer is shaped only by the brand’s site.
Your site matters. But the assistant is pulling from what is available, what is repeated, and what is easy to summarize. That means third-party sources can carry more weight than you would expect, especially if they are direct and opinionated.
A simple way to categorize what tends to shape answers is to think in three buckets.
Owned sources
This is what you control. It’s where you should be unambiguous about identity and fit.
Examples include your core pages, product or solution pages, your help center, and your comparison content. If those pages are vague or inconsistent, the system fills the gaps using whatever else it finds.
Earned sources
This is what other people publish about you: press, directories, partner pages, analyst writeups, and podcasts. Earned sources can reinforce your narrative, but they can also lock in old language and keep it circulating long after you’ve moved on.
If you’ve ever wondered why an outdated detail keeps showing up, this is a common reason.
Crowd sources
This is where a lot of brand perception is built now. Reviews. Forums. YouTube explainers. Community threads. Q&A sites. It’s not always “official,” but it is often clearer than brand copy, which is exactly why it gets pulled in.
Crowd sources can also introduce friction fast. A single thread that frames you as “expensive,” “only for enterprise,” or “hard to implement” can travel farther than you’d expect, because it’s blunt and easy to reuse.
The practical takeaway is not to chase every mention. It’s to identify which sources show up repeatedly in answers about you. Once you know that, you can decide what to reinforce and what to correct.
Chunking is why structure matters
Most teams assume the assistant reads their site like a person reads a page. It doesn’t.
The assistant is far more likely to reuse pieces that can stand on their own. That’s why structure often beats cleverness.
If your best definition is buried mid-page under a vague heading, it’s harder to pull accurately. If your differentiator is implied but never stated, the assistant will replace it with a generic one. If the same concept is described three different ways across key pages, the summary will drift.
You don’t need a giant rewrite to improve this. A few changes usually do more than a full refresh:
Put the definition up front on the pages that matter most
Use headings that label the section clearly
Answer the question directly before expanding into nuance
Keep category language consistent across key pages
Comparison pages are worth calling out here. When buyers ask for alternatives, the assistant is going to answer that question anyway. If you don’t provide a clean, fair comparison, it will pull from whoever did, including competitors.
What this changes for measurement
If you measure AI visibility like classic SEO, you’ll end up frustrated.
These experiences are answer-first. The click is optional. Often it is not even the point.
So instead of treating referral traffic as the only signal, track what is changing in the story and what that story influences downstream.
A practical set of questions to monitor looks like this:
Do you show up for the category and comparison prompts that matter?
Are you described accurately, or does the summary drift?
Are you being used as an input, or just mentioned?
Do sales calls start sounding different?
Do branded search and direct traffic move over time?
You don’t need an enormous prompt library to do this well. In fact, tracking too much tends to create noise. The prompts that matter most are usually tied to fit, alternatives, and buying decisions.
The trap to avoid
The easy mistake is turning this into prompt chasing.
Prompt testing is useful. It shows you what’s happening right now. It can reveal competitor sets, recurring claims, and obvious inaccuracies.
But if all you do is chase outputs, it becomes reactive. You’ll fix one answer, then see it pop up again slightly differently a week later.
The more durable approach is to tighten what the assistant is pulling from. Get the entity right. Make your definitions easy to reuse. Strengthen the pages that define you. Then use prompt tracking as a way to monitor progress, not as the strategy itself.
Where to start
If you want a clean starting point that doesn’t turn into a long project, do two things this week.
First, capture a small set of buying prompts and save the answers as a baseline.
Second, identify what is shaping those answers. Pick the top three sources you see recurring, ideally one owned, one earned, and one crowd source. That’s usually enough to reveal where the leverage is.
If AI visibility is showing up in leadership conversations and you need a plan that is measurable and realistic, Kinetic can help you map what is shaping the answers, tighten the inputs, and focus effort on the changes that actually move outcomes.
