Query fan-out is an AI search technique that turns a question into related searches, retrieves information for each, and combines it into an answer. Google says AI Mode and AI Overviews may use it. For SEO, the practical job is covering the questions behind a purchase, with evidence the search engine can find and the reader can check.
Not more keyword variations. Better answers to different questions.
I'm Brenden House, and I run Searchmaxxed, an SEO and AI SEO agency. Here's what Google has confirmed, what the research actually says, and how to turn it into useful pages.
In summary
- Query fan-out expands the research behind an answer. It doesn't prescribe a page count.
- Google describes related searches across subtopics and data sources. Its published explanation supplies no universal number of searches per prompt.
- Map each buying question to an answer, supporting evidence and a suitable page, rather than publishing every suggested query as a separate article.
- Treat generated query lists as hypotheses unless their source shows an actual search trace.
- Measure coverage, citations and enquiries separately. Being retrieved isn't the same as being recommended.
What does query fan-out mean in Google AI Mode?
Query fan-out means Google can investigate several parts of a question at once before assembling its response. The searcher's wording becomes a starting point.
A subquery is a search generated to resolve part of the original question. It might investigate a comparison, a requirement, compatibility or current availability.
Consider a procurement team asking which maintenance platform suits factories with unreliable connectivity and an existing enterprise resource planning system. That's a purchasing decision, not just a software category.
The useful answer needs more: offline functionality, supported integrations, synchronisation behaviour and implementation requirements. A generic product description leaves those questions open.
Google's AI Mode announcement, dated 5 March 2025, describes searches running “concurrently across subtopics and multiple data sources”.
Its 20 May 2025 explanation supplies the detail. Google describes:
“breaking down your question into subtopics and issuing a multitude of queries simultaneously on your behalf”.
That is the confirmed mechanism. Not a leaked formula.
The same announcement describes Gemini powering the experience, while the March explanation includes Google's Knowledge Graph and product information among its available information sources.
For the wider optimisation work, our Google AI Mode SEO guide covers the search experience beyond query fan-out.
How does query fan-out work, step by step?
Query fan-out breaks a request into useful searches, gathers information, then brings those findings into a response. Google's description also allows the research to change.
The March announcement says the model can “conduct searches to find information and adjust the plan based on what it finds”.
Think of it as research. Finding an integration claim might raise another question: does that integration support the particular workflow this buyer needs, or merely exchange basic records?
| Stage | What happens | What your website needs |
|---|---|---|
| Interpret the request | Identify the decision and its constraints | Clear product, audience and use-case descriptions |
| Generate related searches | Explore different parts of that decision | Answers to specific requirements, comparisons and limitations |
| Retrieve information | Find material across relevant sources | Accessible, indexed pages with useful text |
| Resolve the answer | Combine information and show supporting links | Verifiable claims that make sense outside their original paragraph |
This is a practical reading of Google's explanation, not a complete account of its ranking system. Retrieval means finding information; synthesis means combining it.
Don't confuse the two.
Nor should a diagram quietly invent Google's scoring formula, fixed source count or an obligatory retrieval method that the company hasn't confirmed in these sources.
Do AI Overviews use query fan-out too?
Yes. Google's Search Central guidance says both AI Overviews and AI Mode may use query fan-out.
“May” matters here. The same document says the products can use different models and techniques, so their answers and supporting links can differ for the same search.
Don't merge their results.
For a manufacturing company, record whether its specifications appear in an AI Overview separately from whether AI Mode recommends the product for a particular application.
Both observations are useful. Neither tells you every search Google ran underneath.
Searchmaxxed's AI search work includes tracking what Google's AI answers say alongside the pages and technical fixes that support visibility. The question determines the page.
What do Google's patents and research papers actually prove?
Published patents and papers describe inventions or experiments, not necessarily the system Google currently runs. They help explain possibilities without establishing a production specification.
Keep those categories separate.
Google's Thematic Search patent, US12158907B1, was granted on 3 December 2024. Its abstract describes generating themes:
“generating a plurality of themes from content included in the set of responsive documents”
The sequence is important. The invention obtains search results, identifies themes from their documents and presents thematic results, rather than merely replacing a query with synonyms before retrieval.
Related research explores query generation.
Adolphs and colleagues' query-decoder paper, submitted in October 2022, studies generating queries from a neural retriever's internal representations. Its abstract describes discovering:
“what should have been asked”
The researchers use that phrase to explain how a generated query can retrieve a relevant paragraph that another query might miss. The paper also describes synthetic query reformulations used to train a query-suggestion model.
Useful background, different claim.
| Evidence | What it establishes | What it doesn't establish |
|---|---|---|
| Google's AI Mode announcements | Related searches can run concurrently and inform a combined response | A fixed search count or public ranking equation |
| Search Central documentation | AI Mode and AI Overviews may both use fan-out | Identical behaviour across both products |
| Thematic Search patent | A documented invention that organises retrieved content into themes | Confirmation that AI Mode implements every described step |
| Query-decoder research | A researched method for generating useful query reformulations | The current production design of Google's fan-out system |
The SEO decision doesn't require guessing the missing machinery. Publish the evidence that answers the question, then check whether search engines can find and use it.
How should you map fan-out queries for an enterprise website?
Map the decision first, then assign its sub-questions to evidence and pages. A list of plausible queries alone doesn't tell anyone what to publish.
Start with an actual buying question from sales calls, procurement requests, product enquiries or Search Console, keeping confidential customer details out of your planning examples.
Keep the constraints intact.
A request for manufacturing maintenance software with offline access is different from a request for the cheapest maintenance application. Don't flatten both into “maintenance software”.
A worked example: maintenance software for factories
The following map is hypothetical. It illustrates content planning for an enterprise SaaS company, not observed Google searches or a Searchmaxxed client result.
Original question: “Which maintenance software works offline across our factories and connects to our ERP?”
| Sub-question to resolve | Evidence needed | Best page destination | Fact owner and publication decision |
|---|---|---|---|
| What works without connectivity? | Tested offline actions and limitations | Offline-capability page | Product: publish supported actions, not a blanket claim |
| Which ERP integrations are supported? | Named connectors, supported versions and prerequisites | Integration page | Engineering: distinguish a connector from custom work |
| What happens when devices reconnect? | Synchronisation rules and conflict handling | Documentation linked from the capability page | Engineering: confirm actual behaviour |
| Can access differ by factory? | Roles, permissions and site controls | Enterprise feature section | Product: explain the permission model |
| How is implementation handled? | Scope, dependencies and migration requirements | Implementation page | Delivery: separate customer tasks from supplier tasks |
| Where is operational data stored? | Current hosting and contractual facts | Security or data-hosting page | Security and legal: verify the specific product and market |
| When is this product unsuitable? | Unsupported workflows and constraints | Relevant capability sections | Product: state exclusions beside the feature |
This is where the work becomes useful. You can write missing copy. Adding another FAQ cannot create a capability the product lacks.
Suppose offline support excludes attachments. Say so beside the supported actions, rather than leaving a buyer to discover the restriction after requesting a demonstration.
The same discipline fits manufacturing specifications and public-sector eligibility information: identify the responsible expert, publish the current requirement, and explain where it does not apply.
Market differences deserve evidence too.
For Australian and US audiences, separate genuine differences in availability, hosting, procurement or support. A country name pasted into otherwise identical copy answers nothing new.
Which kinds of sub-question should you look for?
Look for different information needs, not just different wording. These planning categories cover common gaps without pretending to be Google's official classification.
| Question type | Example for the software buyer | Content implication |
|---|---|---|
| Reformulation | Maintenance platform versus maintenance management software | Use familiar alternative terms naturally on the same page |
| Explicit requirement | Offline work-order access | State the supported functionality |
| Implicit requirement | Handling conflicting updates after reconnection | Explain behaviour the buyer needs but may not name |
| Comparison | Native integration versus a custom connection | Compare scope and dependencies |
| Context | Multiple factories with different permissions | Show how the product handles that operating structure |
| Freshness | Whether a connector supports the current release | Date the compatibility information and maintain it |
| Next step | What migration requires | Give the reader a practical implementation route |
Searchmaxxed builds industry, comparison and integration pages within Agentic Search Engineering. That scope suits these distinct questions; it doesn't turn every wording variation into another URL.
Do you need a separate page for every fan-out query?
No. Give a question its own page when it represents a distinct need with enough useful evidence to justify the visit.
Otherwise, improve the existing page.
Use this test: would a reader arriving directly want a complete answer here, or must they return to the main page to understand almost everything?
| Situation | Recommended action |
|---|---|
| Same intent, different wording | Cover the language on the existing page |
| A short clarification of a feature | Add a section or FAQ beside that feature |
| A substantial integration or implementation question | Build a dedicated page and link it to the product |
| A claim without verified evidence | Resolve the factual gap before publishing |
| A requirement the product cannot meet | State the limitation on the relevant page |
A topic cluster is a group of linked pages covering a related subject. It should help readers navigate decisions, not multiply near-identical articles.
Keep each page useful alone.
Link from the product to its integration details, then from those details to implementation requirements, so readers can follow the dependencies without searching your menus.
How do you find real fan-out queries rather than guesses?
Distinguish observed search traces from inferred sub-questions and generated suggestions. They can all inform research, but they are not the same evidence.
Label the difference.
A query generator asked to imagine Google's searches produces suggestions unless it also supplies evidence that those searches occurred in that engine.
| Input | What you can reasonably say |
|---|---|
| An engine's exposed search trace | The engine displayed these searches for this run |
| A tool's extracted queries | The tool reported these queries; check its capture method and engine |
| A model-generated query list | These are plausible research directions |
| People Also Ask or related searches | These are visible Google follow-ups, not proof of AI Mode's internal searches |
| Search Console queries | Search Console reports these search terms associated with your site's performance |
| Sales and procurement questions | These are real customer information needs |
Keep the original wording, market, engine, date and available trace with each observation. Repeat important questions before treating an isolated answer as a stable pattern.
Then inspect the cited page.
Did it answer the main request, supply a specification, explain an exception or compare alternatives? Use that role to decide what your own page needs, rather than copying the heading and hoping the same source selection happens again.
Why can retrieval happen without a citation?
Retrieval puts material into consideration; a citation is a source selected for the answer. Searchmaxxed's own research shows a substantial difference between those stages.
We ran the Australian ChatGPT panel on 2 October 2026, with search enabled. This was our finding:
We put 300 Australian agency-buyer questions to ChatGPT three times each, with search on. It pulled 17,870 search results into those 900 answers and kept 4,389 as sources. Being found is the easy part. About three in four results it reads never make the answer.
That finding is our own. It describes a particular ChatGPT panel, not Google's retrieval rate or a universal result for enterprise searches.
The practical distinction still matters. A retrieved page may supply background without becoming a visible recommendation, so counting discovery alone can hide the part of visibility your business actually needs.
Our article on how modern models choose sources examines source selection separately. Here, the planning task is matching a sub-question to evidence worth using.
How do you optimise pages for query fan-out?
Make the relevant answer easy to find, understand and verify, while keeping the page eligible for Google Search. Fan-out does not bypass technical SEO.
Start with access.
Google's Search Central guidance says a supporting page must be indexed and eligible to appear in Search with a snippet. It also recommends internal links and important content in text.
A compatibility answer hidden inside an inaccessible application cannot do the same public search job as a useful, crawlable page that explains the supported setup.
Then make the answer specific.
- Name the product and requirement in the heading where that helps the reader.
- Answer directly before adding background.
- State the conditions alongside the claim.
- Use tables for comparisons, supported versions and limitations.
- Include the source, fact owner or review date where the information needs verification.
- Match structured data to visible content.
No special fan-out markup exists in Google's guidance. It says there is “no special schema.org structured data that you need to add”.
Schema describes the page. It cannot supply missing evidence.
Searchmaxxed's AI search optimisation service combines this content work with crawl and indexing fixes, schema and internal links. The aim is enquiries.
How should you measure query fan-out SEO?
Measure whether important questions are answered, whether engines use those answers, and whether visitors become relevant enquiries. Keep those outcomes separate in your reporting.
Start with a fixed question set.
Use the same core purchase questions across repeat checks, while recording country, language, product surface and date, so a changed sample doesn't masquerade as progress.
| Measure | What to record | What it tells you |
|---|---|---|
| Question coverage | Important questions with a current, evidenced answer and assigned URL | Whether the site answers the decision |
| Search eligibility | Indexing, crawl access and snippet eligibility | Whether Google can consider the page |
| Observed AI visibility | Named brands, cited URLs and the role each source played | Whether the answer includes your business or evidence |
| Search performance | Relevant queries, impressions, clicks and landing pages | Whether search visibility produces visits |
| Commercial outcome | Relevant enquiries and resulting opportunities where attribution exists | Whether the work helps the business |
For a simple planning measure, divide answered priority questions by all priority questions. Treat that as your coverage measure, not Google's hidden score.
Keep the denominator stable.
Count an answer only when the evidence supports it, the information is current and the assigned page is accessible. A heading alone doesn't qualify.
Search Console helps track search performance, but it is not a complete trace of every internal fan-out query. Use saved answer observations alongside site analytics.
Searchmaxxed connects Search Console and GA4 with page changes and business feedback, then reviews performance against competitors. That keeps the work tied to customers rather than query-list size.
Is Searchmaxxed right for query fan-out SEO?
Searchmaxxed fits companies that need the research turned into pages, technical fixes and ongoing search work. We don't stop at a content-gap presentation.
That matters for enterprise teams.
A product specialist can confirm the integration facts, but those facts still need a suitable page, useful copy, working links and a route to enquiry.
Our managed packages include implementation, direct access to the people doing the work, and client ownership of the website, repository, content and data.
| Plan | Where it fits this work |
|---|---|
| Growth System | Ongoing page improvements, technical SEO, internal links and AI visibility tracking |
| Agentic Search Engineering | Comparison, industry and integration page families, with broader AI answer tracking |
| Custom & Enterprise | Multiple markets, dedicated engineering, custom reporting and original first-party research where scoped |
| Searchmaxxed is a good fit if | Look elsewhere if |
|---|---|
| You need research turned into pages and technical fixes | You want strategy advice with no changes to your site |
| You need ongoing SEO and AI search implementation | Your internal team already handles research, implementation and measurement |
| You need industry, comparison and integration pages | You only want a tool that exports suggested queries |
If your internal team already handles this work, use the map above to organise it.
This also isn't a tool subscription for exporting speculative queries. It's SEO and AI search work for companies that want their products understood and considered.
Frequently asked questions
Are fan-out queries the same as long-tail keywords?
No. Long-tail keywords describe specific search phrases; fan-out queries are searches generated as part of answering another request. Their wording can overlap.
Plan around the underlying question. Don't assume a generated phrase has recurring customer demand.
How many fan-out queries does Google run?
Google's cited announcements do not publish a universal count. The May 2025 announcement says Deep Search can issue hundreds, but that describes a separate research capability.
Don't apply that count to every AI Mode answer.
Does query fan-out replace keyword research?
No. Keyword research helps establish how customers search; fan-out analysis helps identify additional information their questions may require. Use both when deciding what a page should answer.
Does schema markup help with fan-out?
Accurate schema helps describe visible page content. Google says no special schema.org markup is required for AI Overviews or AI Mode.
Fix the answer first. Then mark it up appropriately.
Can People Also Ask reveal Google's fan-out queries?
People Also Ask reveals visible follow-up questions, not a verified trace of AI Mode's internal searches. Use relevant questions as research inputs, not as proof of the mechanism.
How do you optimise a website for AI search?
Publish clear, supported answers on accessible pages, connect related material through internal links, and check what engines actually cite. Keep technical SEO and enquiry measurement in the same plan.
What should you do next?
Choose an important buying question and check whether your website answers its requirements with evidence. Start where the decision currently breaks down.
Missing integration details? Build that answer before commissioning another broad article about the category.
If you want Searchmaxxed to build and maintain those pages alongside your SEO and AI search work, Let's talk pricing.