When you ask ChatGPT to recommend a local business, it doesn't just answer your question. It silently breaks it into 5–10 smaller questions first. The business that answers the most of those wins.
When someone types “best electrician in Brisbane” into Perplexity or ChatGPT Search, something interesting happens before the answer appears. The model doesn’t just look up “best electrician Brisbane.” It fans out — silently running a cluster of related sub-queries to build a fuller picture before it synthesises a recommendation.
That cluster might look something like:
The business that surfaces consistently across the most of those sub-queries is the one that gets named. Understanding this process is the foundation of everything else in AI search optimisation.
The term comes from search engineering. A fan-out query is when a system expands a single request into multiple parallel retrievals to gather diverse evidence before producing a synthesised answer.
Large language models like GPT-4o and Claude use this approach because complex questions can’t be reliably answered from a single source. If someone asks which plumber they should call in their suburb, a single search might return an ad, or a directory with no reviews, or an article from 2019. Running five queries in parallel and weighing what comes back consistently is far more reliable.
For AI models built on retrieval-augmented generation (like Perplexity and ChatGPT Search), this process is visible — you can sometimes see the queries listed as the model “thinks.” For models answering from training data, the process is equivalent but internal: the model is essentially asking itself multiple sub-questions and synthesising what its training data says about each.
Either way, the outcome is the same for local businesses: your visibility isn’t determined by how well you rank for one query. It’s determined by how consistently you appear across a cluster of related queries.
Most local business owners think about their website in terms of what their customers will see when they land on it. Fan-out queries force you to think about a different audience entirely: the AI model that decides whether to recommend you before the human ever visits.
That AI is effectively asking several overlapping questions about your business at once:
Are you real and established? The model looks for consistent mentions of your business name, address, phone number, and services across multiple independent sources. A business that exists only on its own website reads as less established than one mentioned in local news, trade directories, and customer review platforms.
Are you trusted? Reviews are the primary trust signal. The model doesn’t just look at your Google rating — it looks at review volume, recency, the specificity of review content, and whether reviews exist on multiple platforms. Thin or stale reviews reduce confidence.
Are you relevant to the specific query? A plumber who has published content about emergency callouts, hot water systems, blocked drains, and bathroom renovations separately will match more sub-queries than one with a single generic “Our Services” page. Topic specificity matters.
Are you located where the person is looking? Geographic signals — suburb mentions in content, location-specific service pages, a verified Google Business Profile — determine whether the model includes you in a geographically constrained recommendation.
Are you authoritative in your category? Citations from respected sources in your industry signal authority. Being listed in a trade association directory, mentioned in a local news article, or cited on a government resource carries more weight than being listed in a generic link directory.
The reason most local businesses don’t appear in AI recommendations is not that their business is bad. It’s that their digital footprint is too narrow.
They have a website. Maybe a Google Business Profile. A few reviews. But when an AI model fans out and asks five different sub-questions about their category in their city, the business only surfaces in response to one or two of them.
Competitors with a broader digital footprint — more cited, more reviewed, more written about, more specifically described across more sources — appear consistently across four or five of those sub-queries. The model, synthesising that evidence, names the competitor.
The fix isn’t a single action. It’s building coverage across each of the dimensions the fan-out queries probe: citations, reviews, structured content, location signals, and topic breadth. Our AI Search Optimisation service is built around exactly this — mapping the fan-out query landscape for your category and filling the gaps systematically.
You don’t need access to the model’s internal retrieval process. You can map it yourself.
Start with the question your ideal customer would ask Perplexity: “best [your service] in [your city].” Now break that question into every component that matters to someone making that decision:
Each of those becomes a sub-query. And each sub-query is a question you need your website and broader digital presence to answer — directly, specifically, and with supporting evidence from sources beyond your own site.
If you want to see this mapped out for your specific business category and location, an AI Visibility Audit will show you exactly which sub-queries you’re winning and which you’re missing. That’s the starting point for a visibility strategy that actually works.
Can I see the fan-out queries that ChatGPT uses when it searches?
In ChatGPT Search mode (the web-browsing version), you can sometimes see the queries listed while the model “thinks” — they appear as a brief loading indicator before the answer generates. In Perplexity, the sub-queries are often displayed explicitly as “Searching for…” steps. In the base ChatGPT model (without Search mode), the process is internal and not visible, but you can infer it by observing which aspects of a recommendation it addresses in its answer.
Does fan-out querying affect Google AI Overviews as well?
Yes. Google’s AI Overviews (formerly SGE) uses a similar multi-query retrieval approach. When Google generates an AI Overview for a search query, it retrieves content from multiple sources to build the synthesised answer — not just the top-ranked page. Businesses that answer multiple related questions in their content, and that are cited across multiple credible sources, are more likely to be included in AI Overview responses. The same principles that improve your Perplexity visibility also improve your Google AI Overview presence.
How many sub-queries does an AI model typically generate for a local business search?
This varies by platform and query complexity. Perplexity typically shows three to six sub-queries for a local service search. ChatGPT Search can run more in parallel. The number isn’t fixed — it’s determined by the complexity of the original query and what the model needs to build a confident answer. A simple “plumber near me” may fan out to three or four queries; “best rated family solicitor for estate planning in Melbourne” may generate eight or more.
Will optimising for fan-out queries hurt my regular Google SEO?
No — the opposite. Fan-out query optimisation and traditional SEO reinforce each other. Publishing specific, question-led content improves your rankings for long-tail queries in Google. Building citations and external mentions improves your domain authority. Increasing review volume improves your Google Business Profile prominence. The strategy that makes you more legible to AI retrieval systems also makes you more visible in conventional search. There’s no trade-off.
How long does it take to appear in AI recommendations after making changes?
Perplexity and ChatGPT Search retrieve live web content, so new pages can be found within days of being indexed. New citations from external sources are typically picked up within one to four weeks. Google Business Profile updates are reflected quickly. Base ChatGPT model responses (not Search mode) update on training cycles which are less frequent — changes there compound over months rather than days. The retrieval-based platforms respond to improvements faster than most people expect.
Before ChatGPT names a local business, it asks itself a series of sub-questions. Most businesses answer one or two. The businesses that get recommended answer four or five.
Your competitor isn't necessarily better than you. They're just more legible to AI. Here's what Perplexity is actually seeing when it chooses them over you.
Most local businesses haven't started optimising for AI search yet. The ones that act now will build a compounding advantage that latecomers will struggle to close.
Start with a free AI visibility snapshot and see exactly where your business stands across ChatGPT, Perplexity, and Google.
