Guide

How to Get AI to Mention Your Business: The 2026 Australian Playbook

On this page
  1. AEO and GEO fundamentals
  2. Why this matters now
  3. Setting up your llms.txt file
  4. FAQ schema for AI ingestion
  5. Authority and citation strategy
  6. Wikipedia
  7. Industry publications
  8. Podcast guesting
  9. AI brand mention monitoring
  10. Thirty-day action plan
  11. Week one: foundations
  12. Week two: structured content
  13. Week three: authority signals
  14. Week four: measurement and iteration
  15. What comes next

Your customers are no longer typing queries into Google and clicking through ten blue links. They are asking ChatGPT, Perplexity, and Google's AI Overviews for a recommendation, and an answer arrives in fifteen seconds with three named businesses inside it. If your business is not one of those three, the prospect never visits your site. This report shows you how to be one of the three.

Generative Engine Optimisation, or GEO, is the discipline of getting AI search systems to mention your brand. It is the natural successor to traditional SEO. Early industry research suggests the strongest predictor of being cited by an AI is not your domain rating or backlink profile, but the volume of brand mentions across YouTube, Reddit, Wikipedia, and LinkedIn. Backlinks remain a meaningful signal, though not the dominant one. Brand mentions on those platforms appear to weigh more heavily, which changes the playbook.

This guide covers the fundamentals of Answer Engine Optimisation and GEO, the technical setup that makes your site machine-readable, the authority signals that AI systems weight most heavily, and a practical thirty-day plan you can hand to a junior marketer or run yourself on weekends. Recommendations draw on public statements by Google, OpenAI, and Perplexity, and on industry research into AI citation patterns published in the last twelve months.

AEO and GEO fundamentals

Answer Engine Optimisation is the older of the two terms. It refers to the practice of structuring content so that a search engine can lift a complete, self-contained answer out of your page and present it as a featured snippet, voice answer, or "People Also Ask" expansion. AEO has been a working tactic since 2016. Generative Engine Optimisation is the 2024 evolution: structuring content and earning signals so that a generative AI system, ChatGPT or Perplexity or Google's AI Overviews, will cite your brand by name in its synthesised response.

The two disciplines overlap heavily but are not identical. AEO rewards a clean answer in the first forty words of a section. GEO rewards everything AEO rewards plus a strong entity footprint, plus accessibility to AI crawlers, plus content quotable in passages of roughly one hundred and fifty words. AI systems prefer self-contained passages because they need to extract a chunk of text, attribute it, and paste it into a response without inheriting context from the surrounding page.

The other crucial distinction is platform behaviour. Google's AI Overviews tend to draw most of their citations from pages already ranking in the top ten organic results, which means traditional SEO is the price of entry. ChatGPT pulls a large share of its citations from Wikipedia, with a smaller share from Reddit, so entity presence on those platforms matters alongside your own domain authority. Perplexity leans even harder on Reddit. Overlap between platforms is small, so optimising for "AI" as a single channel does not work; each platform's selection logic differs.

Why this matters now

Google's AI Overviews now appear on a large share of queries and reach hundreds of millions of users globally. ChatGPT and Perplexity each handle very large monthly query volumes. Industry monitoring through 2025 also reported a sharp jump in AI-referred sessions to publisher sites. The traffic is not theoretical. It is here, it is growing, and the sites that captured it early did the structural work in 2024.

Setting up your llms.txt file

The llms.txt file is the AI-era equivalent of robots.txt. It sits at the root of your domain, lists the most important pages on your site, and gives AI crawlers a clean structured map of what to ingest. The standard was proposed by Jeremy Howard in late 2024 and has been picked up by a growing list of AI labs and publishers.

Your llms.txt should live at https://yourdomain.com.au/llms.txt. It is a plain text file with a specific Markdown-like format. Here is a copy-paste template you can adapt in five minutes:

# Your Business Name

> One sentence describing what your business does and who it serves. Keep it under thirty words.

## Core services
- [Service one page title](https://yourdomain.com.au/services/service-one/): Short description of what this service includes and who it is for.
- [Service two page title](https://yourdomain.com.au/services/service-two/): Short description of what this service includes and who it is for.
- [Service three page title](https://yourdomain.com.au/services/service-three/): Short description of what this service includes and who it is for.

## About
- [About us](https://yourdomain.com.au/about/): Founding date, location, team size, key credentials.
- [Case studies](https://yourdomain.com.au/case-studies/): Named client outcomes with measurable results.

## Frequently asked
- [Pricing FAQ](https://yourdomain.com.au/faq/pricing/): How we price, what is included, payment terms.
- [Process FAQ](https://yourdomain.com.au/faq/process/): How an engagement runs from enquiry to delivery.

## Key facts
- Founded: 2019
- Location: Sydney, Australia
- Team size: 12
- Industries served: SaaS, professional services, e-commerce
- Notable clients: [Client name], [Client name], [Client name]

Three rules govern a strong llms.txt. First, list only your best pages: twenty links is plenty, and sixty dilutes the signal. Second, write descriptions that read like a human summarising the page, because that is exactly what an AI will use them for when deciding whether to ingest the link. Third, include a "Key facts" block with structured factual claims about your business. Wikipedia editors will use this when verifying entity claims, and AI systems will treat it as a high-trust source.

Once the file is live, validate it by visiting the URL and confirming it serves as plain text with a 200 status. Check that GPTBot, ClaudeBot, and PerplexityBot can reach it by confirming your robots.txt does not block them. The most common mistake is publishing llms.txt and then accidentally blocking the very crawlers that read it.

FAQ schema for AI ingestion

Structured data is the second pillar. AI systems use Schema.org markup to extract facts confidently. They will cite a page that declares "this is a Question, this is the Answer, this is the AggregateRating" far more readily than a page with the same information buried in prose, because the structured version removes ambiguity.

The single highest-leverage schema for AI visibility is FAQPage. It tells the AI exactly which question on your page answers which query, and the AI can lift that answer block verbatim. Every service page on your site should carry an FAQPage block with three to six questions, each with an answer of roughly one hundred and fifty words. Industry research suggests passages around that length are the most likely to be extracted cleanly: shorter often feels incomplete, longer gets truncated.

Here is a working FAQPage block. Drop it inside the <head> of any service page, edit the questions and answers, and you are done:

<script type="application/ld+json">
{
  "@context": "https://schema.org",
  "@type": "FAQPage",
  "mainEntity": [
    {
      "@type": "Question",
      "name": "How much does this service cost in Australia?",
      "acceptedAnswer": {
        "@type": "Answer",
        "text": "Write a one hundred and fifty word answer here that includes a specific price range, the factors that move the price up or down, what is included at each price point, and a one-line note on payment terms."
      }
    },
    {
      "@type": "Question",
      "name": "How long does an engagement take?",
      "acceptedAnswer": {
        "@type": "Answer",
        "text": "Write a one hundred and fifty word answer here that gives a typical timeline, the factors that affect the timeline, what happens in each phase, and what the client needs to provide."
      }
    }
  ]
}
</script>

Beyond FAQPage, the other schemas worth implementing are Organization on your homepage with a complete sameAs array pointing to your LinkedIn, Wikipedia, YouTube, and Crunchbase profiles; Person schema on every author bio with credentials and external links; and LocalBusiness schema with address and opening hours if you serve customers from a physical location. Validate every schema with Google's Rich Results Test before deploying. A broken schema block is invisible to AI systems and frustrating to debug.

Authority and citation strategy

This is where most businesses lose. They build a clean website, ship working schema, publish their llms.txt, and then wonder why ChatGPT still recommends three competitors. The reason is almost always that those competitors have a stronger entity footprint outside their own website. AI systems weight third-party signals heavily because those signals are harder to manufacture than self-published content.

Four entity surfaces move the needle. Wikipedia is the highest-trust source for ChatGPT and a meaningful source for every major AI. Wikidata is the structured-data layer underneath Wikipedia and is read directly by every AI system. Industry publications, particularly trade press in your vertical, provide the citation depth that distinguishes a real business from a website. Podcast guesting builds the YouTube and audio mention footprint that industry research identified as the single strongest predictor of AI citations.

Wikipedia

You cannot create a Wikipedia article about your own business. You will be reverted within hours by an editor, and the attempt will harm your future chances. What you can do is earn a Wikipedia article by being notable enough to attract one. Notability on Wikipedia means being the subject of significant coverage in multiple independent reliable sources. Three feature articles in the Australian Financial Review, the Sydney Morning Herald, or trade press like Marketing Magazine and B&T will typically meet the bar. Once those exist, a request at Wikipedia's Articles for Creation queue, written by a third party, has a reasonable chance of success.

If a full article is not realistic in the next twelve months, focus on Wikidata instead. Wikidata accepts entries for businesses with much weaker notability requirements, and a clean Wikidata entry gives ChatGPT and Claude a structured fact to work with. Add your business as an item, link it to your website, your industry, your founding date, your location, and your CEO's Wikipedia or LinkedIn profile. The exercise takes an hour and pays dividends for years.

Industry publications

The trade press in your vertical is the most underused authority surface in Australian SEO. Editors at SmartCompany, Inside Small Business, Mumbrella, B&T, and Marketing Magazine actively look for expert sources. Pitch one original data point you can share, one contrarian opinion on a current debate, and one named client case study. Two pitches a month, sustained for six months, can land you in several publications depending on relevance and timing. Each placement adds an authoritative third-party citation that AI systems can verify.

Podcast guesting

Podcasts are the highest-leverage authority play right now because YouTube auto-transcripts of podcast episodes feed directly into the corpus AI systems use to identify expert voices in a given vertical. A single sixty-minute appearance produces a YouTube video, an audio file, a transcript, and a set of show notes, each indexed independently. Aim for one appearance a month. PodMatch and MatchMaker.fm are commonly used booking platforms. Pitch with a specific topic, three concrete data points, and a recent client story.

AI brand mention monitoring

If you are not measuring AI mentions, you are guessing. The good news is that monitoring tools matured rapidly through 2025 and there are now usable free options alongside the paid suites.

The free baseline is a weekly spot check by hand. Pick the ten queries a prospective customer would actually type into ChatGPT to find a business like yours. Ask each query in ChatGPT, Perplexity, Claude, and Google AI Overviews. Note which businesses appear in each response. A simple spreadsheet with queries down the rows and platforms across the columns will show you in fifteen minutes whether your visibility is changing month over month. Run it on the first of each month and you have a free monitoring system.

The paid options bring scale and consistency. Several AI-monitoring platforms are usable from Australia as of early 2026. They run many queries a day across multiple AI systems and produce a dashboard of your share of voice, the queries you appear on, the queries your competitors appear on, and the source pages each AI is citing. Pricing starts around US$99 a month for a single brand and rises with query volume. They are worth evaluating if you can name three concrete actions you would take with the data; otherwise the manual spot-check approach above is enough to start.

Thirty-day action plan

Block ninety minutes a week and follow this checklist. By day thirty you will be ahead of the vast majority of Australian businesses on AI visibility.

Week one: foundations

  • Audit your robots.txt. Confirm GPTBot, OAI-SearchBot, ChatGPT-User, ClaudeBot, and PerplexityBot are all allowed. If they are blocked, fix it today.
  • Publish your llms.txt at the root of your domain. Use the template in this guide. Twenty links maximum.
  • Add Organization schema to your homepage with a complete sameAs array linking to every external profile you own.
  • Run the manual ChatGPT and Perplexity spot check on ten queries. Save the spreadsheet. This is your baseline.

Week two: structured content

  • Pick your three highest-value service pages. Add an FAQPage schema block with four questions each. Each answer between one hundred and twenty and one hundred and seventy words.
  • Rewrite the opening forty words of each of those service pages to lead with a definition: "X is..." or "X refers to..."
  • Add a comparison table to one service page. Structured tables tend to be cited more readily than equivalent prose.
  • Validate every schema block with Google's Rich Results Test before deploying.

Week three: authority signals

  • Create or update your Wikidata entry. Link to your website, industry classification, location, founding date, and any executive Wikipedia profiles.
  • Pitch three trade publications. One data point, one opinion, one case study. Use named editors, not the generic newsroom email.
  • Apply to two podcasts via PodMatch or MatchMaker.fm. Specific topic, three data points, recent client story.
  • Update your top three author bios with credentials, headshots, and external links to LinkedIn and any speaking appearances.

Week four: measurement and iteration

  • Run the manual ChatGPT and Perplexity check again on the same ten queries. Compare to the week one baseline.
  • Identify the single query where your share of voice grew the most. Document what changed.
  • Identify the single query where a competitor mentions a source page you do not have. Plan that page.
  • Decide whether to subscribe to a paid AI monitoring tool. The threshold is simple: if you can name three actions you would take with the data, the tool will pay for itself.

What comes next

The next thirty days will earn you a measurable lift in AI visibility for the queries you already partially own. The thirty days after that should focus on building original research. A small client survey, a benchmark study of public data in your vertical, or a primary analysis of your own anonymised customer data, published with a clear methodology and shareable charts, is the single most-cited content type across every AI system and will continue earning citations for years.

Week one establishes your technical foundation: crawl access, llms.txt, and Organization schema. Week two converts your best service pages into AI-quotable structured content. Week three builds the off-site entity footprint that separates cited businesses from invisible ones. Week four gives you the measurement loop to know what is working and where to press next.