Generative Engine Optimisation (GEO)

When generative AI systems answer a question, they read many sources and synthesise a single piece of text. People often read that text and make their decision without clicking through to the sources. Visibility is then measured not by a ranking but by whether the brand's name appears in the answer, whether it is described accurately and whether it is cited as a source.

The term GEO gained currency through an academic paper published in 2023. The study found that content containing quotable statements, verifiable sources and clear statistics appeared more often in generative answers. In practice, GEO rests on clear definitions, consistent organisation details, trustworthy third-party mentions and versions of content that machines can read easily. Because many AI systems look up sources with a RAG approach while generating answers, being findable is still the first requirement.

• Quotable content: Short, verifiable sentences that make sense on their own: definitions, comparisons and clear conclusions.
• Entity consistency: The organisation's name, description, services and contact details are the same across the site, profiles and directories.
• Third-party signals: The brand is mentioned in the right context in industry publications, directories and community platforms.
• Machine-friendly access: Channels through which AI agents reach content directly, such as llms.txt, Markdown versions and MCP.
• Freshness: Dated, regularly updated content that stops outdated information from resurfacing in answers.

When someone asks an AI assistant "which digital agencies in Istanbul build AI products?", the answer lists a few brands with short descriptions. Whether a brand appears in that list, which sentence describes it and whether that sentence is accurate is what GEO is about.

That is why globalmeta.net offers its content to AI systems in several ways: the llms.txt and llms-full.txt files at the root summarise the site, every page has a Markdown version with a .md extension, and a read-only MCP server runs at /mcp. The server gives AI clients tools to search the site, list pages, fetch a page as Markdown and retrieve contact details.

Measurement uses a fixed set of questions. Brand, category and comparison questions are asked at regular intervals across different AI systems, and the brand's mention rate, citation rate, share of visibility against competitors and the accuracy of the information in the answers are recorded. Because answers change with personalisation and model updates, trends are tracked rather than one-off checks.

In addition, referral traffic from AI sources in analytics, visits from AI crawlers in server logs and changes in branded search volume are assessed together.

AEO aims to be the source of a direct answer to a question and grew out of the era of featured snippets and voice assistants. GEO focuses on the longer answers that generative models synthesise from multiple sources; here, brand mentions without links, how the brand is described and how it is positioned alongside competitors also matter. The two disciplines share the same foundation and are usually run together in practice; our AI-native system design and digital ecosystems services build that foundation.