AEO and GEO for Australian Businesses: How to Get Recommended by ChatGPT and Google AI Mode

To get recommended by ChatGPT and Google AI Mode, an Australian business needs five things in place: a clearly defined entity that AI systems can identify, published evidence that answers real buying questions, pages that are technically easy to extract, third party endorsement (reviews, directories, press, citations), and a tracking loop that tells you which prompts you appear in. AI answers are assembled from search retrieval, not from a ranking list, so the work is closer to being the most quotable source on a topic than to chasing a keyword position.

That is the short version. Below is the long version: how the two engines actually pick businesses, what we measured across 24 buying intent prompts in our own tracker, the framework we use with clients, and a 90 day plan you can run yourself.

How do ChatGPT and Google AI Mode decide which businesses to name?

Both engines work the same way at a high level. Your question is decomposed into several sub questions, each sub question triggers a search, the returned pages are read, and the answer is written from what those pages say. Google calls this query fan out and documents it in its guide to optimising for AI features in Search. ChatGPT’s browsing and search mode does the same thing against its own index and web results, described in OpenAI’s documentation of ChatGPT search.

Three consequences follow, and they are the whole game.

1. There is no single query to rank for. “Best accountant Brisbane” might fan out into “top rated accounting firms Brisbane”, “accountants for small business Brisbane CBD”, “how much do accountants charge”, “questions to ask an accountant” and “accountant reviews Brisbane”. You can be invisible for the headline phrase and still be named because you own three of the sub questions.

2. The engine has to be able to read and lift your answer. If the fact the model needs is inside a slider, an image, a PDF, or a paragraph that takes 400 words to arrive at the point, it usually will not survive retrieval. Extraction favours short, direct, self contained statements sitting under a heading that matches the question.

3. Corroboration beats assertion. Models weight claims that appear in more than one place. A statement that only exists on your own website is a claim. The same statement echoed on your Google Business Profile, in a directory, in a supplier listing or in a news article becomes a fact the model is comfortable repeating.

Google’s own position is worth quoting plainly, because it kills a lot of expensive nonsense: AI Overviews and AI Mode run on the normal Search index. There is no separate AI index to submit to. AEO and GEO are SEO, done with a different output in mind. If someone is selling you an AI ranking submission service, that is the tell.

What we measured: 24 buying intent prompts, run on our own brand

We do not like publishing advice we have not tested on ourselves, so Titan Blue runs a weekly AI visibility tracker against our own name. Every Monday it fires a fixed set of buying intent prompts at Google AI Mode and Perplexity, records whether we are named, whether our domain is cited as a source, and which competitors are recommended instead.

The run on 30 August 2026 put 24 prompts through the engines. Here is what came back, unedited:

• 10 answers were usable. Titan Blue was named in 2 of them, a 20 per cent mention rate, up from 2 of 12 (17 per cent) two days earlier.
• Our domain was cited zero times. Being named and being cited are different outcomes, and the second one is harder.
• 14 prompts produced nothing usable. All 12 Perplexity prompts hit an anonymous rate limit, and 2 Google prompts hit an unusual traffic wall. We exclude these rather than scoring them as misses.
• Competitors named instead: one Gold Coast agency appeared in 4 answers, two others in 2 each, and three domains we had never seen before turned up in a single run.

The most useful finding was not the score. It was the shape of the answers. On local “best X in {suburb}” prompts, Google AI Mode returned business cards with the star rating and review count quoted verbatim, straight from Google Business Profiles, plus one descriptive sentence lifted from the business’s website. The top named agency carried a 5.0 rating with more than 250 reviews. On the non local, concept led prompts (“how do I get my business recommended by AI”, “what is answer engine optimisation”), the engine did the opposite: it quoted articles and cited domains.

That single observation reorganises the whole strategy, and it is the reason the framework below separates the two.

The Five E Framework for AI recommendation

Five E is the working method we use on Titan Blue accounts. It is deliberately ordered: each layer is close to useless until the one above it is in place.

E1. Entity: can an AI system tell exactly who you are?

Before a model can recommend you it has to resolve you to a single, unambiguous business. That means one consistent legal and trading name, one address format, one phone number, and the same details on your website, your Google Business Profile, your ABN listing, LinkedIn and every directory you appear in. Two trading names, an old suburb on a directory and a disconnected number are enough to split you into two weak entities instead of one strong one.

Practical checklist: publish name, address, phone and service area in text on the site (not only in an image or a footer graphic), add Organization or LocalBusiness structured data, list the suburbs you genuinely serve, name your founders and key staff, and state the year you started. Titan Blue has been operating since 2001, and that single dated fact appears identically everywhere we are listed. Ambiguity is the enemy here, not modesty.

E2. Evidence: publish answers, not brochures

Service pages describe what you sell. Evidence pages answer what buyers ask before they are ready to buy, and those are the pages that get retrieved during fan out. The gap in most Australian small business websites is enormous: eight service pages, one about page, zero pages that answer a question in the customer’s own words.

Build the question list from sources that are real rather than imagined: the questions in your inbox and quote calls, Google Search Console queries that already bring impressions, the “people also ask” box, your live chat logs, and the objections your sales team hears weekly. Then write one page per genuine topic, answering the sub questions inside it as separate H2 and H3 sections. Do not spin every phrasing variation into its own thin page. Microsoft draws the same line for Bing and Copilot, which is the index behind Copilot answers, in its Bing Webmaster Guidelines: write clear, original, self contained pages rather than near duplicate variations. Google names that specific behaviour, creating separate content for every variation of how people might search, as a scaled content abuse violation in its spam policies.

E3. Extractability: make the answer liftable

This is where most of the technical work sits, and where a website build either helps or quietly sabotages everything else. The rules are unglamorous:

• Answer the question in the first 40 to 60 words under the heading that asks it. No run up.
• Use headings that read like questions a person would type or say.
• Keep facts in text. Prices, hours, service areas, specifications and inclusions inside images, PDFs or JavaScript widgets are invisible to most retrieval.
• Render server side. If the content only appears after client side JavaScript executes, you are gambling on whether the crawler that fed the answer bothered to render it.
• Add FAQPage structured data for genuine questions, and keep the schema honest and matched to visible content.
• Keep pages fast. Server response time above roughly 800 milliseconds is the most common bottleneck we find on Australian small business hosting, and it affects crawl depth as much as user experience.
• Date your content and keep it current. Models prefer sources that show a recent, specific timestamp over undated evergreen mush.

E4. Endorsement: the corroboration layer

For local prompts this is the single biggest lever, and our tracker data makes the case better than any argument. Google AI Mode read star ratings and review counts straight out of Google Business Profiles when answering “best agency in {suburb}”. No blog post competes with that. If you want to be named on local best of prompts, the work is review volume, review recency and a complete profile, not another article.

What actually moves it: a fully completed Google Business Profile with correct categories, services, hours, photos updated monthly and posts published regularly (see Google’s guidance on profile quality); a repeatable review request that goes out at the moment of delivery rather than a month later; replies to every review, positive and negative; and consistent listings on the directories that actually matter in Australia, which is a much shorter list than the ones sold to you in bulk.

Off your own property, the same logic extends to being mentioned in industry publications, supplier and partner pages, association member lists, and any local media that covers your sector. Those are the corroboration sources a model leans on when deciding whether to repeat a claim.

E5. Engine feedback: measure the prompts, not the rankings

You cannot manage what you do not track, and rank tracking does not track this. Build a fixed list of 10 to 25 buying intent prompts, phrased the way a customer would say them out loud, run them on a fixed schedule, and record three things: were you named, was your domain cited, and who was recommended instead. Keep the prompt list stable so the numbers are comparable week to week.

One measurement discipline matters more than the rest: a block is not an absence. Rate limits, login walls and captcha pages must be recorded as unusable and excluded from your mention rate. The first version of our own tracker counted blocked answers as misses and reported a completely fake zero per cent visibility. Any tool or agency reporting AI visibility to you should be able to say how many of the runs were blocked.

Local prompts and concept prompts need opposite work

Almost every wasted AEO budget we see comes from applying one lever to the wrong prompt type. Sort your prompt list into two buckets before you spend anything.

A dental practice that wants to be recommended in “best dentist Robina” should spend the next quarter on reviews, not blogging. A national B2B software company that wants to be named in “best inventory system for Australian wholesalers” should spend it on comparison and documentation content, because there is barely any map data to lean on. Most businesses need both, in a deliberate ratio, not a blur.

What this means for how your website is built

Any agency can design a beautiful website. The question that decides whether it earns AI recommendations is whether the thing under the design is readable, fast and structured. We have audited sites where the entire service description, the pricing table and the service area list existed only inside an image slider. Visually excellent. Completely unquotable.

The build decisions that matter for AI readiness:

• Content in HTML, not in graphics. Every claim you want repeated must exist as selectable text.
• Server rendered pages. Page builders that assemble content client side can leave a crawler with an empty shell.
• One clear H1 and a logical heading tree. Headings are the extraction map.
• Structured data that matches the page. Organization, LocalBusiness, Product, FAQPage where genuine.
• Performance budget enforced at build time. Fewer scripts, compressed images, real caching, and a host that answers quickly.
• An accessible contact path. An enquiry form that is present in the HTML, not injected by a third party script that a bot never runs.

This is why we treat AI readiness as a build requirement rather than an optional extra on our web design and business website projects, and why we offer a standalone AI ready check for sites that were built before anyone was thinking about this. Retrofitting extractability into a site built as a brochure is possible, but it is always more expensive than building it in.

How to track your own AI visibility

Start manual and cheap. Once a month, open a fresh browser session (logged out, so you are not seeing personalised results), run your 15 prompts through Google AI Mode and ChatGPT, and paste the answers into a spreadsheet with three columns: named, domain cited, competitors listed. That is a real dataset within two months and it costs nothing but an hour.

Then add the supporting instrumentation:

• Referral traffic from AI sources. In GA4, segment referrals from chatgpt.com, perplexity.ai, copilot.microsoft.com and gemini.google.com. Volume is usually small; intent and conversion rate are usually excellent.
• Search Console queries that read like questions. Rising impressions on long conversational queries are the earliest signal that your evidence pages are being retrieved.
• Brand mention monitoring. Track your business name across the web, since corroboration is what upgrades a claim into a repeatable fact.
• Review velocity. Reviews added per month per profile, tracked as a KPI, because for local prompts it is the KPI.

Set expectations honestly. Movement in AI answers is lumpier and slower than rank tracking. Our own mention rate moved from 17 per cent to 20 per cent in one week on a smaller denominator, which is noise, not progress. Judge this on a quarterly trend line with a stable prompt set.

Five mistakes that waste the budget

1. Buying an “AI submission” service. There is no index to submit to. Google states plainly that AI features are powered by the same Search systems and the same crawling and indexing rules.

2. Publishing an llms.txt file and calling it strategy. No major answer engine has committed to consuming it as a ranking or retrieval input. It costs nothing to add and it changes nothing on its own.

3. Writing for models instead of people. Keyword stuffed, robotic, “AI optimised” prose performs worse, because retrieval quality follows the same content quality signals as everything else. Write the clearest possible answer for a human and you have written the extractable one.

4. Ignoring reviews while blogging furiously. If your priority prompts are local, and most Australian small business prompts are, you are pulling the wrong lever with great enthusiasm.

5. Fabricating authority. Invented statistics, fake case studies and made up awards get contradicted by other sources, and contradiction is exactly what a retrieval system is good at detecting. Original first hand material, even small and unglamorous, outperforms borrowed credibility.

A 90 day plan for an Australian business

Days 1 to 30: entity and baseline

• Write down 15 buying intent prompts your customers would actually say. Run them once and record the results. This is your baseline, and it will be uncomfortable.
• Audit name, address, phone and service area across your site, Google Business Profile, LinkedIn, ABN listing and every directory you can find. Fix every inconsistency.
• Add or correct Organization or LocalBusiness structured data.
• Fix the extractability basics on your top ten pages: direct answer under each heading, facts in text, headings phrased as questions.
• Measure server response time and page weight. If the server takes more than about 800 milliseconds to respond, that is your first infrastructure job.

Days 31 to 60: evidence and endorsement

• Publish four deep evidence pages, one per genuine topic cluster, each answering five to eight sub questions inside the one page.
• Include something first hand in every page: a real project, a measured number, a local specific, a genuine customer quote.
• Launch a review request process at the point of delivery. Reply to every review inside 48 hours.
• Publish Google Business Profile posts weekly and refresh your photos.
• Fix your top three directory listings and remove or correct any stale ones.

Days 61 to 90: expansion and measurement

• Re run the prompt set. Compare named, cited and competitor lists against the baseline.
• Expand the pages that are being retrieved rather than starting new ones. Depth on a working page beats a new thin page every time.
• Pursue two or three genuine third party mentions: an industry publication, a supplier case study, a local association profile.
• Set the cadence: prompts monthly, content fortnightly, reviews continuously.

Two habits decide whether this compounds. First, keep the prompt list fixed so the trend line means something. Second, expand before you multiply, because depth on a page that is already being retrieved is worth more than a new page that is not.

Frequently asked questions

How long does it take to get recommended by ChatGPT or Google AI Mode?

Entity and extractability fixes can show up within weeks, because they change how existing pages are read. Content authority and review driven local visibility typically take one to two quarters. Anyone promising AI recommendations in 30 days is either lucky or selling something.

Is AEO different from SEO?

Not fundamentally. AI features run on the same Search index and the same crawling and indexing rules, so the foundations are identical. What changes is the target output: instead of optimising for a click on a blue link, you are optimising to be the sentence the model repeats and the source it cites. That shifts emphasis to direct answers, entity clarity, corroboration and structured content.

Does my business need to be on Google Business Profile to be recommended?

For any local intent prompt, effectively yes. In our own tracking, Google AI Mode built its local recommendations directly from profile data, quoting ratings and review counts verbatim. Without a complete, active profile you are not a candidate for that class of answer at all.

Do reviews really affect AI recommendations?

For local prompts they are the dominant factor we can observe. The businesses named in our tracked answers carried high ratings across large review counts, and the answers cited those numbers as the reason. Review volume and recency are the most reliable lever available to a local Australian business.

Should I create an llms.txt file?

It is harmless and takes ten minutes, but treat it as housekeeping rather than strategy. No major engine has committed to it as a retrieval input. Spend the time on extractability and reviews instead.

Can I track whether ChatGPT mentions my business?

Yes, with a fixed prompt list run on a schedule and logged. Do it manually in a spreadsheet, or automate it. The essential discipline is separating blocked or rate limited runs from genuine non mentions, otherwise your visibility score is fiction.

What if my competitors are already being recommended and I am not?

Read the answers rather than the score. The engine usually explains why it named them: a review count, a specialisation, a page it could quote. That explanation is a to do list. In our own tracking, the agencies named ahead of us won on review volume for local prompts and on published depth for concept prompts, which told us exactly which two things to work on.

Where to start

If you do one thing this week, run your 15 prompts and write down what comes back. Most Australian businesses have never seen how they are described, or not described, by the systems an increasing share of their customers now ask first. The gap between that answer and the one you would want is your brief.

Titan Blue has been building websites for Australian businesses since 2001, and we now build and audit them for AI visibility as standard. If you want a second opinion on where you stand, our answer engine optimisation and generative engine optimisation services start with exactly the prompt level audit described above, and you can get in touch to see your own baseline.