Most Australian SEO agencies have renamed a service without rethinking it. They’ve swapped “rankings” for “AI visibility” in their pitch decks, kept the same keyword-density playbook, and hoped nobody checks the results.
The problem is structural, not cosmetic. Traditional SEO optimises for a ranked list. AI SEO optimises for inclusion in a synthesised paragraph. These are different problems with different solutions, and conflating them produces the worst of both: keyword-stuffed content that satisfies neither crawlers nor retrieval pipelines.
What the models actually reward
When ChatGPT, Perplexity, Gemini or Claude generates a recommendation, it is not reading your meta title. It is drawing on a retrieval stack that weighs three things above almost everything else: entity confidence, third-party corroboration, and topical authority depth.
Entity confidence means the model knows, unambiguously, who you are — your name, your category, your location, your people. This is a schema and knowledge-graph problem, not a content-quantity problem.
Third-party corroboration means your name appears, consistently and in context, across sources the model treats as authoritative: trade press, industry lists, podcasts, academic and government sites, community forums. The link is secondary. The mention is primary.
Topical authority depth means your domain is the most comprehensive, most structured source on the specific questions buyers ask in your category. Not the most posts. The most useful, machine-readable answers to the exact conversational queries buyers run.
Why keyword tools don’t help here
The standard agency workflow — pull keywords from Semrush, cluster them, brief a writer, build out a content calendar — produces content optimised for queries humans type into a search box. Buyers increasingly don’t do that. They ask full questions to AI systems that synthesise answers from many sources at once.
The buyer who once Googled AI SEO agency Melbourne now asks Claude: We’re a mid-sized B2B SaaS in Melbourne. Which agencies actually understand generative-engine optimisation, and what should I look for? The model’s answer isn’t determined by keyword density. It’s determined by which entities it has high confidence in, and which sources corroborate them.
The content type that actually gets cited
In our portfolio, the highest-cited content types — measured by how often they appear verbatim or near-verbatim in LLM outputs — are:
- Definitional pages (“What is X”) with DefinedTerm schema
- Comparison pages that acknowledge genuine weaknesses (not marketing fluff)
- FAQPage schema-marked content answering the exact questions buyers ask LLMs
- Case studies with specific, verifiable numbers
- Glossaries of domain-specific terminology
Notice what’s not on that list: general blog posts optimised for a keyword cluster. Those still have a role in traditional SEO. They don’t drive AI citations.
What to look for in an AI SEO agency
Ask any agency you’re evaluating two questions. First: how do you measure success? If the answer involves keyword rankings, they’re doing traditional SEO with an AI label. If the answer involves citation rate across specific LLMs for specific buyer queries, they understand the actual problem.
Second: can you show me a content piece that got cited in a generative answer? Specifically, the query, the model, and the verbatim or near-verbatim output. If they can’t, they’re guessing.
The shift from ten blue links to one synthesised answer is the same kind of structural change as the shift from print directories to search engines. The companies that understood early that Google was a different medium — not just a better phone book — built compounding advantages. The same window is open now. It won’t be for long.