R-U-KnownBusinesses do not need another collection of disconnected marketing acronyms. They need to know what each activity is meant to improve, what evidence to look for and where the work fits in the customer journey.
R-U-Known focuses on the measurable outcome: when a potential customer asks a relevant question, can AI find your company, understand what it does, include it in the answer and recommend it over the alternatives?
The simple difference
| Term | Main focus | Business question |
|---|---|---|
| LLMO | Making business facts clear to large language models. | Does AI understand who we are, what we offer and where we operate? |
| AEO | Structuring content so answer engines can extract useful answers. | Can AI understand and summarise our answer to a customer question? |
| GEO | Improving visibility in generative search experiences. | Will our content or brand appear as a useful source in an AI answer? |
| AIO | Improving how customers interact with AI on owned channels. | Can an AI assistant help a customer take the right next step? |
| AI search visibility | Measuring discovery, presence, recommendation, sources and competitors. | What did AI actually show, recommend or ignore—and what changed? |
The terms overlap, but they are not interchangeable. LLMO and AEO describe work done to make information clearer. GEO describes the broader effort to earn visibility in generative experiences. AI search visibility is the measurement layer that shows whether the work is changing the outcome.
What is LLMO?
LLMO, or Large Language Model Optimisation, is the work of making accurate business facts easy for AI systems to connect and understand. Those facts include the company’s name, services, locations, audience, team, products, proof, policies and contact details.
The practical goal is not to manipulate a model. It is to reduce uncertainty. When the same accurate facts are clearly explained across a company website and trusted public sources, AI has better context for identifying the entity and its relevance.
What is AEO?
AEO, or Answer Engine Optimisation, focuses on the quality and structure of answers. An AEO-ready page addresses a real customer question directly, gives the short answer early, and then provides the detail, conditions and evidence needed to make that answer useful.
For example, instead of only saying that a company offers a service, an answer-focused page can explain who the service is for, what it includes, how long it takes, what affects the price and what the customer should do next.
What is AIO?
AIO, or AI Interaction Optimisation, focuses on what happens when a customer is already interacting with an AI assistant on a website, in a support tool or through a channel such as WhatsApp. It is less about being discovered in search and more about making the conversation accurate, useful and connected to a realistic next step.
A strong AIO experience knows the approved business facts, asks useful follow-up questions, avoids making unsupported promises and hands the customer to a person when the request needs human judgment.
What is GEO?
GEO, or Generative Engine Optimisation, is the broader practice of improving how a business and its content are represented in generative search. It can involve useful articles, strong service pages, third-party sources, clear entity information, structured content and evidence that supports the company’s claims.
GEO should not be treated as a promise of guaranteed citations or recommendations. AI systems choose answers dynamically. The sensible goal is to make the business more relevant, understandable and supportable in the situations that matter.
Where AI search visibility fits
Visibility measurement asks what happened in the actual answer environment. It tests a consistent set of customer-relevant questions and records whether the business was:
- Invisible: AI did not retrieve or surface the business for the question.
- Discovered: AI found evidence of the business but did not recommend it.
- Recommended: AI presented the business as a relevant option.
- Competitive: another company appeared or was recommended instead.
- Grounded: specific pages or domains helped shape the answer.
A practical order of operations
The most useful sequence is to build clarity first, then measure the result:
What should a company measure?
Do not rely on a single mention count. A useful benchmark should show the questions tested, the exact answer, whether the company was found or recommended, which competitors appeared, and which sources were connected to the result.
R-U-Known turns these results into a record of movement. Teams can see where they are absent, where competitors win, which sources repeatedly shape answers and which actions deserve attention next. The goal is a clearer decision about what to improve—not a vanity score.
Common questions
Is GEO the same as AI search visibility?
No. GEO is the improvement work intended to strengthen generative visibility. AI search visibility is the measurement of what answer engines actually do with the business across relevant questions.
How is AIO different from AEO?
AEO helps content become a clearer answer in search and answer engines. AIO improves the conversation after someone begins interacting with an AI assistant on an owned channel.
Do AEO and LLMO guarantee AI recommendations?
No. Clear information and answer-focused content can improve understanding, but no provider can guarantee that an independent AI system will cite or recommend a brand.
Which one should a business start with?
Start with accurate business facts and the questions customers actually ask. Then benchmark the current answer space so content work is connected to a measurable opportunity.
See what AI knows about your business.
R-U-Known benchmarks the questions that matter, shows where your brand appears or disappears, and connects the evidence to practical next actions.
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