AI search & GEO — get cited by the models.

There is a quiet shift happening in how Australians find small businesses online. Search volume on Google is still enormous, but an increasing share of "what's the best plumber near me" questions are being asked to ChatGPT, Perplexity, and Google's AI Overviews. Those tools don't just rank websites — they read them, summarise them, and decide which ones to cite. A business that shows up as a cited source in an AI answer captures that prospect. A business that doesn't, effectively doesn't exist for that search.

This cluster covers the emerging discipline of Generative Engine Optimisation — the practical techniques for making your website citable by Large Language Models. It starts with the structural foundations: llms.txt, semantic HTML, passage-level citability, and the schema markup that AI crawlers actually read. It moves through content patterns that trigger citations (answer-first paragraphs, stable H2 IDs, clean factual density, named entities).

We also cover entity optimisation — the Wikipedia and Wikidata work that moves your business from being a random URL to being a recognised entity in the Knowledge Graph — and the emerging robots.txt conventions for AI crawlers (GPTBot, PerplexityBot, ClaudeBot, Google-Extended). Plus the uncomfortable realities: AI citations are noisier than Google rankings, attribution is harder to measure, and the ROI case is still maturing. But the traffic that does arrive converts exceptionally well because the LLM has already pre-qualified the prospect.

Start with the pillar guide. Spokes will cover llms.txt best practices, entity optimisation, the difference between Google SGE and ChatGPT Search, and specific tactics for each major AI platform.