GEO vs SEO. The actual difference.
SEO and GEO share technical foundations but optimise for different outcomes. SEO earns you a position in a ranked list of links. GEO earns you a citation inside a synthesised answer. The best GEO work sits on top of a healthy SEO foundation, not in place of it.
How generative engines choose what to cite
Every generative AI platform runs on some version of retrieval-augmented generation. When a user asks a question, the model retrieves relevant content from indexed sources and structured data, then synthesises an answer that quotes or paraphrases those sources. Being cited depends on three things.
Retrieval. The model can find your content in the first place. Crawlability, schema, and entity clarity make this possible.
Interpretation. The model can understand what your content says and who it applies to. Atomic answer structure and consistent entity signals make this possible.
Trust. The model considers your source worth citing. External references, structured data, and consistency across the open web make this possible.
GEO checklist. 8 things to fix on your site this month.
1. Fix your entity
A single canonical Organization schema, referenced from every page by @id. Consistent name, description, location, founders, and services across your site, Google Business Profile, LinkedIn, and any directory that lists you. Entity confusion is the number one reason AI systems fail to cite established businesses.
2. Add JSON-LD structured data on every route
Organization, Service, FAQPage, BreadcrumbList, Article, DefinedTerm. This is the layer generative engines read to understand what a page is about and how it connects to the rest of your business.
3. Rewrite key pages as atomic answers
Each H2 is a question. Each paragraph is a self-contained answer that could be quoted without context. Generative engines cite what they can lift cleanly, not what they have to paraphrase.
4. Build authority pages for the terms in your category
Definitional pages, comparison pages, and how-to pages that own the language of your industry. These are the pages AI systems reference when they need a source.
5. Open access to AI crawlers
Explicitly allow GPTBot, ClaudeBot, PerplexityBot, Google-Extended, and the other major AI user agents in robots.txt. A default-allow works too, but explicit is safer and gets you into the index faster.
6. Earn references from sources AI trusts
Category publications, industry directories, guest posts, podcast appearances, and expert roundups. GEO is not purely on-site work. External references are still what tips the balance on close calls.
7. Measure monthly across four platforms
Run a fixed set of category prompts across ChatGPT, Claude, Gemini, and Perplexity. Score presence, accuracy, framing, and recommendation. Trend the results. Without measurement you are optimising in the dark.
8. Iterate on the weakest gap first
Weakest platform first. Weakest metric first. Ship the fix. Re-measure next month. GEO compounds through repetition, not through single big launches.
How long GEO takes to work
Most established businesses see initial AI citations within 4 to 12 weeks of shipping the entity and schema layer. Sustainable share of voice across the major generative engines typically builds over 6 to 12 months as authority pages and external references compound.
GEO is a longer game than paid ads but shorter than most SEO plays. Generative engines retrieve fresh sources faster than Google re-ranks them, so well-executed fixes tend to show up quicker than traditional SEO would suggest.
GEO for Australian businesses. What is different.
Generative engines localise. When a buyer in Melbourne asks ChatGPT to recommend a service in their category, the model weighs Australian sources, en-AU language, and local context. An Australian business optimised only for the US market often ends up invisible in its own country.
Australian GEO work adds hreflang tags, en-AU inLanguage on structured data, and references from Australian directories, publications, and industry bodies. It also factors in Australian competitive sets, which are usually different from the US players AI systems default to.