AI Search Visibility Agency — Gold Coast & Australia-Wide
Language models are the new front door. If they can't retrieve you, understand you and verify you, they name someone else.
LLM SEO is the practice of making a business easy for large language models to retrieve, understand and cite. It covers three things a model checks before naming anyone: whether your content answers the question directly, whether your entity record is consistent across the web, and whether independent sources corroborate what you claim.
A search result gives ten options and lets the buyer choose. A language model gives two or three and calls it a recommendation. That is a far shorter list, arrived at by different rules — and most businesses have never checked whether they are on it.
Systematic optimisation for retrieval-augmented models: clean crawl access, structured data a model can parse, an unambiguous entity record, self-contained answers, and the third-party evidence that turns a claim into a fact worth repeating.
The major assistants retrieve differently and weight sources differently. Optimising for one is not the same as optimising for all — but the foundations are shared.
| Assistant | How it retrieves | What it favours | Crawler |
|---|---|---|---|
| ChatGPT | Live web search plus training data | Clear, quotable pages with visible corroboration | GPTBot, OAI-SearchBot |
| Google Gemini | Google's own index | Conventional ranking strength plus structured data | Google-Extended |
| Google AI Overviews | Google index, surfaced in results | Pages already ranking well with extractable answers | Googlebot |
| Perplexity | Live retrieval, always cited | Recency, direct answers and clean source attribution | PerplexityBot |
| Claude | Live web search plus training data | Well-structured, factually careful sources | ClaudeBot |
| Copilot | Bing index | Bing ranking strength and schema | Bingbot |
Retrieval behaviour changes as these products ship. We re-test platform by platform rather than assuming last quarter's behaviour still holds.
Confirm GPTBot, ClaudeBot, PerplexityBot, OAI-SearchBot and Google-Extended can actually reach every page — permission in robots.txt is worthless if the server refuses them.
A fixed prompt set run across every major assistant, recording who gets named and in what order.
Schema that states plainly what you are, where you operate and what you sell.
One canonical record — name, address, phone, identifiers — reconciled everywhere it appears.
Answers written to be lifted whole: self-contained, specific and free of preamble.
Reviews, directories, press and industry mentions that independently verify your claims.
A clean machine summary of your site, kept current as pages change.
Catching and correcting what models get wrong about you before customers read it.
Verify every AI crawler can reach your content — the step most audits skip.
Run the prompt set and record exactly what each model says today.
Fix schema, headings and answer shape so content can be retrieved and quoted.
Build the independent evidence models check before naming a business.
Repeat the prompt set monthly and act on what moved.
The full programme covering Google and every AI assistant together.
Platform-specific work for the assistant with the largest audience.
Earning citations inside AI-generated content.
The record that tells machines exactly who you are.
We test before we theorise. Every engagement starts with a fixed prompt set run across ChatGPT, Gemini, Claude, Perplexity and AI Overviews, so we know exactly which models name you, which name competitors, and what each one gets wrong about your business.
Headquartered on the Gold Coast with a Brisbane location, working Australia-wide.
Scoped on the number of platforms tracked, how many prompts and competitors are monitored, and the size of the content and entity workload.
How AI decides which businesses to recommend and entity SEO explained.
LLM SEO is the practice of making a business easy for large language models to retrieve, understand and cite. It covers the structure and clarity of your content, the consistency of your entity record across the web, and the independent sources that corroborate what you claim — the three things a model checks before naming anyone in an answer.
Most assistants now retrieve live web results before answering rather than relying only on training data. They favour sources that answer the question directly, are structurally clear, and are corroborated elsewhere — consistent business details, reviews, directory listings and third-party mentions. Ambiguity is the main reason a business is skipped.
They overlap heavily and are often used interchangeably. LLM SEO describes optimising for the models themselves, AEO focuses on winning direct-answer placements, and GEO focuses on being cited inside generated content. In practice the same underlying work — structure, entity clarity and corroboration — serves all three.
It is a proposed standard, not an established ranking factor, and no major model publicly commits to reading it. It costs almost nothing to publish and gives crawlers a clean summary of your site, so it is worth having — but it will not compensate for weak structure, a thin entity record or an absence of third-party corroboration.
You can block specific crawlers such as GPTBot, ClaudeBot, PerplexityBot and Google-Extended in robots.txt. For most businesses this is the wrong trade: blocking them removes you from the answers your customers are reading. The better strategy is to be quoted accurately and attributed, not to be absent.
You test it systematically. We run a fixed set of buying-intent prompts across each major assistant, record which businesses are named and in what order, and repeat monthly so movement is visible. Ad-hoc checking is unreliable because model responses vary between sessions.
Structural and schema changes are picked up within days to weeks by retrieval-based assistants. Entity and citation work typically shows in answers over one to three months. Anything relying on a model's underlying training data moves far more slowly, which is why retrieval signals are where the effort belongs.
Citations, then structure, then content volume. A model will skip a beautifully written page from a business it cannot corroborate. Get the entity record consistent and the third-party evidence in place first; structure and depth compound on top of that foundation rather than substituting for it.
