GEO for Australian Businesses: How to Get Cited by ChatGPT, Perplexity and AI Overviews

# What GEO actually is, and why it is not SEO with a new name

Generative Engine Optimisation is the practice of getting your business named inside an AI-generated answer, rather than listed on a page of links. The distinction matters because the two formats have completely different economics. A search results page shows ten organic results and you can profit from eighth. An AI answer names two or three businesses. There is no eighth place.

That single structural difference explains most of what follows. SEO is a ranking problem: you compete for a position in an ordered list, and the list is long enough that steady improvement pays. GEO is a selection problem: you are either in the sentence or you are not mentioned at all. Improvement is not linear, and being "nearly good enough" returns nothing.

The second difference is what the engine is reading. Google's crawler indexes your page and ranks it against a query. A generative engine assembles an answer from what it understands about entities - businesses, people, places, services - and the relationships between them. It is not asking "which page best matches these words." It is asking "which business do I know enough about to confidently name."

SEO earns you a position in a list of ten. GEO earns you a mention inside a sentence that names two or three. That is why AI visibility does not reward incremental improvement the way search rankings do: there is no eighth place in a paragraph.

# The number that should change your mind

If you take one figure from this guide, take this one: only 12% of the URLs cited by AI search engines rank in Google's top 10 for the same prompt. That is Ahrefs' analysis of AI search overlap, and it dismantles the most common assumption Australian business owners hold about AI search - that being good at Google makes you good at ChatGPT.

It gets sharper. In a separate Ahrefs study of how ChatGPT's citations compare to Google's results, ChatGPT showed the lowest overlap of any assistant tested at roughly 6.5%. Whatever ChatGPT is doing when it decides who to name, it is not reading Google's rankings and copying them.

For local service businesses the gap is starker still. SOCi's 2026 Local Visibility Index, reported by Search Engine Land, analysed nearly 350,000 locations across 2,751 multi-location brands. ChatGPT recommended 1.2% of them. Gemini recommended 11%, Perplexity 7.4%. The same brands appeared in Google's local 3-pack 35.9% of the time. Localogy's write-up of the same report puts the difficulty gap at three to thirty times harder than traditional local search.

Read those two findings together and the conclusion is uncomfortable but clear. Your Google ranking is not a proxy for your AI visibility, and it is not a leading indicator of it either. They are close to independent, and most Australian businesses are measuring only one of them.

# What Australian search actually looks like in 2026

Australia is not a laggard market for AI search - it is an early one, which means the cost of waiting is higher here than the global average suggests. DataReportal's Digital 2026 Australia report finds 64% of Australians have already encountered an AI Overview in search results.

The same data shows what those encounters do to click behaviour. Around 65% of Australian Google searches already end without a click. When an AI Overview is present, that rises to 83%. The searcher gets their answer on the results page, and the business that would have earned the visit never learns it existed.

Ahrefs quantified the cost to the top result. Across 300,000 keywords, the presence of an AI Overview correlated with a 34.5% lower click-through rate for the top-ranking page against comparable keywords without one. eMarketer's coverage of the study put the same figure in front of a general business audience. Ahrefs' later December 2025 data puts the reduction closer to 58%, so the trend since has not been kind.

None of this means Google traffic has stopped mattering. It means the ceiling on it has come down, and the compensating channel - being named in the answer itself - is one most Australian businesses have never deliberately competed for.

# How an AI engine decides who to name

An AI engine names a business when it has enough consistent, corroborated information to be confident the business exists, does the thing being asked about, and serves the area in question. Confidence is the operative word. A model that is unsure will name someone it is sure about instead.

That confidence is assembled from a handful of inputs, and they are not the inputs SEO trained you to optimise:

• Entity clarity. Can the engine tell what your business is, unambiguously, from your own site? Structured data is how you say this in a form a machine cannot misread. Google's own structured data documentation is the reference implementation.
• Corroboration elsewhere. Does anyone other than you say the same thing? A business described identically on its own site, a business profile, a directory, a review platform and a news mention is a business a model will name.
• Reviews and their recency. For local queries, review volume and rating act as a confidence floor. Below it, engines tend to name someone else.
• Answerable content. Pages that state a specific answer in a self-contained paragraph get quoted. Pages that build to a conclusion over 800 words do not.

Notice what is absent from that list: keyword density, backlink count, and the volume of content published. Those are ranking inputs. They are weak citation inputs.

If you have never seen what the engines currently say about your business, that is the cheapest place to start. Our free AI visibility audit runs the prompts your customers actually use and reports where you appear, where a competitor appears instead, and the three gaps closing them fastest. It comes back within 48 hours and costs nothing.

# The six levers that actually move citations, ranked by evidence

Ranked by how much measured evidence supports them, the levers are: third-party corroboration, reviews, answer-first content structure, crawler access, entity and structured data, then everything else. The ordering matters more than the list, because most AI-search packages sold in Australia are priced in almost exactly the reverse order.

1. Third-party corroboration. Mentions of your business on sites you do not control. This is the lever with the least tooling and the most leverage, and it is why a business with a thin website and a strong external footprint often gets named ahead of a business with the reverse.

2. Reviews. Volume and rating, on the platform that dominates your category. For most Australian service businesses that is Google. Reviews are simultaneously a trust signal to humans and a corroboration signal to machines.

3. Answer-first content structure. Lead each section with the answer in a self-contained 40 to 60 word paragraph, then explain. A model extracting a quotable passage takes the one that stands alone.

4. Crawler access. Confirm the AI crawlers are not blocked in robots.txt. Strictly this is a precondition rather than a lever: it adds nothing on its own, but nothing above it survives being blocked. A five-minute check that occasionally reveals a catastrophe.

5. Entity clarity and structured data. Lower than most people expect, and lower than we first placed it. Structured data is how you tell an engine which business you are rather than letting it infer, and it is mechanically required for rich results - but a controlled test of 1,885 pages found that adding it moved AI citations barely at all. It is necessary work with a real return; it is not the citation lever it is sold as. We set out the evidence in what schema markup actually does.

6. Everything else. Including, specifically, the file most vendors lead with. See the next section.

If you would rather work the list yourself than hire it out, the AI Search Readiness Kit is the same 61-check framework we run internally, at $29. If you want it installed rather than explained, the AI Search Foundation is a fixed-scope five-day install at $2,990.

# What does not work, and who is selling it to you

The clearest example of a lever sold far above its evidence is llms.txt - a file that tells AI crawlers how to read your site, which the available data suggests almost nothing currently reads. We publish one. We also do not think you should pay much for it, and we would rather say so here than have you discover it after an invoice.

We will publish the full analysis separately, because it deserves its own piece and its own numbers. The summary: adoption across large domain samples sits around ten per cent, direct crawler fetches of the file are vanishingly rare against total AI bot traffic, and no major vendor has committed to supporting it. It is a sensible twenty-minute hygiene item. It is not a strategy, and any proposal that leads with it is a proposal to check carefully.

Two more to treat sceptically. Volume alone. Publishing more pages does not raise citation rates if the entity underneath is still ambiguous - and on a young site it carries real risk under Google's spam policies on scaled content. And "AI-optimised" as a bare claim. The ACCC has been explicit that AI-related claims must be substantiated like any other; a vendor who cannot tell you what they measured and when is making a claim they may not be able to support.

Publishing an llms.txt file is a twenty-minute hygiene item with close to no measured effect on citations. Any AI search proposal that leads with it, rather than with entity data and third-party corroboration, is priced around the least effective lever available.

# How to tell whether any of this is working

Measure mention rate against a fixed prompt set, on a fixed schedule, and ignore everything else for the first quarter. The mistake is to look for AI traffic in analytics. Most of it is invisible: sessions arrive without a referrer, and the searches that never produce a click cannot appear at all.

What you can measure honestly is whether the engines name you. Write down twenty to thirty prompts a real customer would type - "best [your service] in [your suburb]", "who should I use for X in Brisbane", "is [your business] any good" - and run them on a schedule across the engines that matter. Record whether you were named, who was named instead, and in what position. That series, tracked monthly, is the only clean signal available.

Two honest caveats. Results vary between runs, so single readings mean nothing and you need several months before a trend is real. And the timeline is slow: entity and corroboration work compounds over quarters, not weeks. Any vendor promising AI visibility in thirty days is describing something other than what the evidence supports.

One practical warning about the prompt set: write it before you start optimising, and then do not change it. The temptation, three months in, is to quietly swap out the prompts you keep losing for ones you win. That converts your only honest measurement into a marketing exercise. If a prompt turns out to be badly worded, retire it explicitly and note the date, so the series stays readable rather than silently improving.

The volumes will look small next to Google, and that is expected. AI referral traffic is low-volume and high-intent - someone arriving from an answer that named you has already been pre-sold by a system they trust.

# The mistake that quietly costs Australian businesses citations

The most common reason a capable Australian business never gets named is not that the engine judged it poorly - it is that the engine could not work out which business it was. This is an entity resolution failure, and it is invisible in every tool most businesses use.

It happens most often to businesses whose name is a contested token. If your trading name is a common word, an abbreviation, or shares a string with a larger organisation overseas, an engine with weak grounding will resolve the name to whichever entity it knows best. That is rarely the Australian small business. You can be the top organic result for your own brand name and still lose the entity, because ranking and resolution are different mechanisms.

We know the failure mode well because we have it. "Calnet" collides with several unrelated organisations, including a United States telecommunications network, and an engine that is not confident will name one of those before a Brisbane web agency. Publishing that is more useful than pretending otherwise, and fixing it is a large part of what our own entity work consists of.

Three symptoms worth checking for. The engine describes you as something you are not - usually the profile of the organisation it confused you with. It gets your location wrong, which is almost always a sign it has merged you with a similarly named business elsewhere. Or it says it has no information, despite your site ranking perfectly well, which means the entity exists in the index but carries no confident attributes.

The repair is unglamorous and effective: state the same facts about the business in the same words everywhere a machine can read them, including the legal entity name and the ABN, and connect those statements to each other so a model can follow the chain. ABS data on business use of information technology shows how much of the Australian small business sector now depends on a single digital touchpoint - which is precisely why an ambiguous one is expensive.

There is a fairness question underneath this that is worth naming. Entity confidence rewards businesses that already have a footprint, which means AI search currently favours incumbents more than Google's index does. The practical response is not to complain about it but to build the footprint deliberately, starting with the two or three surfaces in your category that engines actually read.

# Where to start: the first 30 days

In order: find out what the engines say about you now, fix your entity data, then go and get corroborated. The sequence matters, because corroboration built on an ambiguous entity does not compound.

Week one - measure. Write the prompt set. Run it. Record the baseline before you change anything, because the baseline is what makes the improvement provable later.

Week two - fix the entity. Structured data on the homepage first, then your main service pages. Confirm your business name, address, phone and services are stated identically everywhere they appear. Check robots.txt is not blocking AI crawlers.

Week three - claim the profiles. Google Business Profile completed properly, not just claimed. Bing Places, which matters more than its market share suggests because of what reads it. Then the two or three directories that genuinely operate in your category.

Week four - start the corroboration flywheel. Ask recent customers for reviews. This is the least technical and most effective work in the month, and it is the part most businesses skip because it feels like admin rather than marketing.

None of this requires a rebuild. If your site is genuinely too thin to carry it, that is a different conversation - our breakdown of what a website costs in Australia and the Brisbane buyer's guide both cover when a rebuild is and is not justified. But most Australian service businesses do not have a website problem. They have an entity problem, and it is cheaper to fix.