Generative Engine Optimization (GEO) is the practice of making an entity - person, company or organization - readable, citable and authoritative for AI answer engines and search systems, by structuring data and digital identity at the source rather than competing on traditional keywords.
While classical SEO focuses on rankings in search engines to capture human attention on a web page, GEO operates at the semantic layer. Its immediate target is not the end user, but the large language model (LLM) synthesizing the information. A brand or professional no longer needs to just hope they appear in a list of links, but must provide structured, verifiable proof so that the AI can cite them with confidence.
Optimizing for generative search means transitioning from textual persuasion to data architecture. Through the coordinated use of JSON-LD, Schema.org relationships, and the integration of llms.txt files, a company's identity stops being a guess to interpret and becomes a fact to process. This hub gathers the notes, practical cases, and fundamentals to guide the transition to AI Readiness.
From marketing to human customers to Business to Agent (B2A): why the next recipient is an AI agent that reads, evaluates and acts - and why brands win by declaring their identity in structured data. With the Patagonia case.
The underlying principle behind all other notes. What entity linking is, why it is not entity resolution, and how it defines the Semantic Entity Architect role.
How to build a machine-readable semantic identity for AI engines without a Wikidata item, using JSON-LD, Schema.org, llms.txt and official profile mappings.
Why customers look for suppliers and solutions by asking AI, and what a business needs to be found and cited correctly in AI-generated responses.
If you share a name with other people, AI systems can merge your histories. The fix isn't a better bio - it's building a verifiable entity.
Nonprofits are among the least readable entities for AI systems. Being found and cited accurately doesn't require more content - it requires structure.
I work with professionals, businesses and organisations to design the data infrastructure needed to be found, understood and cited correctly by artificial intelligence systems.