Query fan-out tracking: the searches behind every AI answer
Query fan-out tracking shows the web searches an AI engine ran while writing its answer to a buyer question. Proofsource records each distinct search per prompt and per engine, counts how often it repeats, and shows search demand across answers. SEO and content teams use it to see which searches they must rank for to get cited by ChatGPT and Claude.
| Prompt | Searches | Avg per answer |
|---|---|---|
| best ai meeting notes app for sales teams | 8 | 3.0 |
| otter alternatives | 6 | 2.5 |
| fireflies vs fathom | 5 | 2.5 |
| meeting recorder with crm sync | 4 | 2.0 |
- otter vs fireflies vs fathom 2026
- ai notetaker hubspot salesforce integration
- meeting notes app pricing per seat
- best ai notetaker reddit
Query fan-out tracking, in short
The facts, ready to quote.
- What it shows
- The web searches an engine ran to write each answer
- Grouped by
- Prompt, with a search count and average searches per answer
- Engines
- Recorded whenever the engine reports its searches; ChatGPT and Claude often do
- Gaps shown
- Engines that did not report searches are shown as not reported
- Search demand
- Counts of searches engines ran, shown only when enough brands share them
- Per answer
- Open any answer to see its searches and its sources together
Query fan-out, explained
Query fan-out is what an AI engine does when one question is too broad to answer from memory. It splits the question into several smaller web searches, reads the results and writes one answer from them. A question like "best AI meeting notes app for sales teams" might fan out into "Otter vs Fireflies 2026", "AI notetaker CRM integration" and "meeting notes app pricing".
Those hidden searches decide which pages the engine reads, and so which brands it can cite. If your pages do not rank for the fan-out searches, the engine never sees them, however good they are. That is why fan-out queries have become the new keyword list for answer engine optimization (AEO) and generative engine optimization (GEO).
Proofsource captures the searches each engine reports for each answer, keeps every distinct search per prompt, and counts them across your answers. You see the most repeated searches, which engines ran them, and how many searches each answer used on average. When an engine does not report its searches, Proofsource says so rather than guessing.
How it works
See the web searches ChatGPT and Claude ran before answering each buyer question, so you know what to rank for.
Ask the tracked question
Each buyer question runs on every engine you track.
Record the searches
Proofsource saves each search the engine reports for that answer as evidence.
Group by prompt
Distinct searches are grouped under the question that triggered them and counted.
Show demand
Searches that repeat across answers rise to the top, with the engines that ran them.
Act on it
Use the top searches as targets for pages that answer them directly.
See it in the product
Real screenshots from the Proofsource app show a live brand; drawn screens carry sample data for an example brand.
Counts shown only when enough brands share them.
Every capability, live today
3 shipped capabilities behind query fan-out tracking, each in the product now.
Search evidence per answer
Each answered run keeps the searches the engine reported, so you can see what it looked up before it answered.
Distinct searches per prompt
Every distinct search a prompt's answers ran is listed once and counted, grouped under the prompt.
Engine search demand
A count of the searches AI engines ran while answering, shown only when enough brands share it so no one brand is exposed.
Who it is for
Turn fan-outs into a keyword list
Target the searches engines run, not just the question buyers type.
Write pages engines can find
Brief writers on the exact searches behind questions where you are not cited.
Explain a lost answer
Show a client the searches that surfaced competitors and the pages that ranked for them.
Learn the buyer's frame
Fan-out searches show which features and comparisons engines think matter.
Why it matters
See the hidden step
Fan-outs show where engines look before they decide who to name.
Clear targets
Repeated searches tell you which pages to build or improve first.
No guessing
Engines that do not report searches are marked not reported, never filled in.
Private by design
Shared demand counts appear only when enough brands share them.
Google AI OverviewsGoogle AI Mode
Query fan-out tracking: Proofsource and other tools
Query fan-out data is offered by a growing number of tools. Here is how each describes it.
| Capability | Proofsource | Peec AI | Otterly.AI | Ahrefs | Rankscale |
|---|---|---|---|---|---|
| Fan-out searches per prompt | YesDistinct searches per prompt and engine, counted | YesShows web searches engines run before answeringSource | YesExpands a prompt into related queriesSource | YesShows sub-queries an AI generates behind a promptSource | YesShows internal searches engines run before answeringSource |
| Searches shown with the answer and sources | YesAnswer detail shows searches and cited sources together | Not listed | Not listed | Not listed | Not listed |
| Search demand from engine searches | YesCounts of searches engines ran, privacy-safe | Not listed | Not listed | RelatedSearch demand report: keywords that trigger AI answers in GoogleSource | RelatedEstimates prompt volume and intentSource |
| Engines whose searches are shown | YesChatGPT and Claude, whenever the engine reports its searches. | YesShows web searches engines run before answering; filter by type and topicSource | YesExpands a prompt into related queries; ChatGPT, AI Mode, AI Overviews; also in GEO AuditSource | YesShows sub-queries an AI generates behind a prompt, to find content gapsSource | Not listed |
Where each of them shines
Fan-out searches you can filter by type and topic. Compare in full
Fan-out across ChatGPT, AI Mode and AI Overviews, also inside its GEO Audit and free tools. Compare in full
Fan-out sub-queries next to Ahrefs' deep keyword and search demand data. Compare in full
Fan-out searches plus prompt volume estimates in one product.
Questions about query fan-out tracking
What is query fan-out in AI search?
It is when an AI engine splits a buyer's question into several web searches, reads the results and writes one answer. The searches decide which pages the engine sees and can cite.
Which engines show their fan-out searches?
Proofsource records searches whenever an engine reports them. ChatGPT and Claude often do. When an engine such as Google AI Overviews or Perplexity does not report its searches for an answer, Proofsource marks it as not reported.
How do I use fan-out queries for SEO?
Treat the most repeated searches as target keywords. If your page ranks for the searches an engine runs, the engine is far more likely to read and cite it.
Can I see the searches for a single answer?
Yes. Open any answer to see the searches the engine ran and the sources it cited, side by side.
What is engine search demand?
It is a count of the searches AI engines ran while answering questions in your category. To protect privacy, a count is shown only when enough brands share it.
How is this different from Peec AI or Ahrefs fan-out data?
Peec AI, Otterly.AI, Ahrefs and Rankscale all show fan-out searches, which is great for the market. Proofsource groups each distinct search under its prompt, counts it, shows it next to the answer and sources, and links it to the fixes that follow.
Does fan-out tracking cost extra?
No separate add-on. Searches are recorded from the answers you already track, which are charged in credits for work that ran.
Keep exploring
Try it on your own brand.
Proofsource is live: scan your brand and see query fan-out tracking at work with your own answers, competitors and sources.
Google AI OverviewsGoogle AI Mode