Large Language Model Optimisation
AEO and GEO are about specific answers. LLMO is broader: making sure everywhere a model can reach for information about your brand actually says something true, current and specific.
What LLMO means in practice
Models don't invent facts about your brand out of nowhere. When they get something wrong, it's usually because the actual information either doesn't exist anywhere accessible, exists but contradicts itself across sources, or is thin enough that the model fills the gap with a plausible-sounding guess.
LLMO is the umbrella work underneath AEO and GEO. Where AEO focuses on a single answer and GEO focuses on a blended one, LLMO is about the underlying pool of content itself: is there enough of it, is it accurate, and does it agree with itself across your site, your documentation, third-party listings and anywhere else a model might look.
This includes older or thin pages that nobody's touched in years, contradictions between your marketing copy and your support documentation, and gaps where a reasonably obvious question about your product simply has no clear answer published anywhere.
Where hallucinated details usually come from
We've seen models confidently describe a pricing tier that was discontinued two years ago, or attribute a feature to a product that never had it, because the only content that ever existed on the topic was a single vague blog post from years earlier and nothing since to correct it.
This isn't really the model being unreliable in some abstract sense. It's a content gap with a confident-sounding answer stapled on top. The fix is almost always the same: publish something clear, specific and current enough that there's no gap left to guess at.
How an LLMO engagement works
Retrieval-source mapping
We identify the places a model is likely pulling information about you from: your own site, documentation, Wikidata, review platforms, forums and industry directories.
Gap and contradiction audit
We check for questions that have no clear published answer anywhere, and for places where two sources describe you differently.
Content correction and creation
Thin or outdated reference content gets rewritten, and genuine gaps get filled with clear, specific, current material.
Cross-source alignment
Where contradictions exist between your own site and third-party listings, we work on getting them to agree, since a model has no way to know which version is right.
Periodic hallucination checks
We prompt models directly on a recurring basis and check the factual accuracy of what comes back, then report honestly on drift over time.
What you actually get
- A map of where models are likely retrieving information about your brand
- A gap and contradiction report across your key sources
- Rewritten or newly created reference content to close the gaps
- Cross-source alignment recommendations for third-party listings
- A recurring hallucination check with plain findings, not just a score
Works well alongside
- Entity SEO, to give models one consistent identity to retrieve facts from.
- Knowledge Graph Optimisation, for the structured, machine-native facts that reduce guesswork.
LLMO questions worth answering honestly
Does LLMO mean getting my content into a model's training data?+
Can I get a model to "forget" wrong information about my brand?+
How is LLMO different from technical SEO?+
Do open-source and smaller models matter for LLMO work?+
Not sure what a model actually knows about you?
A free audit shows exactly how ChatGPT, Gemini and Perplexity answer your category today, and where Large Language Model Optimisation could close the gap.
or call 1300 138 708