Clarify what your business is
Create consistent language for the company, services, expertise, markets and relationships that must be understood.
Optimization starts with clarity and evidence—not tricks.
LLMO and GEO are overlapping practices for improving how content and brands can be understood, retrieved, cited and represented by large-language-model and generative search experiences.
There is no single switch that makes a model recommend a company. Durable work aligns clear first-party information with credible external evidence, technically accessible pages and buyer-relevant answers.
Answer real buyer questions
Build self-contained, specific pages that resolve category, comparison, selection and proof questions without vague marketing copy.
Strengthen corroborating evidence
Identify the credible independent sources, expert signals and references that support important claims about the business.
Test before and after
Track a controlled prompt set over time, noting mentions, accuracy, citations and competitive share rather than claiming deterministic rankings.
LLMO and GEO questions, answered
Terminology changes quickly. The underlying business requirement is more stable: be clear, credible, accessible and useful wherever buyers research.
Is LLMO the same as SEO?
No, but they overlap. Sound technical SEO, useful pages and credible links can support both. LLMO also examines generated answers, entity interpretation, citations and recommendation contexts across multiple AI systems.
Can schema markup make an LLM recommend my company?
Schema can help search systems understand explicitly described information, but it is not a recommendation guarantee. It should accurately represent visible content and sit alongside strong evidence and useful pages.
Do we need an llms.txt file?
It can provide a concise machine-readable map of key pages, but it is experimental and not a proven ranking or citation lever. It should complement—not replace—crawlable HTML, a sitemap and clear internal linking.
How do you measure progress?
We use a defined prompt set and record observable outcomes such as mention presence, recommendation share, factual accuracy, cited sources and competitive visibility across repeat test periods.