A new open standard designed to help AI systems understand website knowledge more accurately has entered a 33-day public consultation. EntityMap, developed by the team at Waikay, aims to give organizations a way to publish a structured, machine-readable map of what they do, what they offer, how their key entities relate to one another, and where the supporting evidence sits on their website.
The specification is available at entitymap.org/spec/v1.0. The consultation runs until 30 June 2026, with the official launch scheduled for 1 July 2026. Developers, publishers, structured-data specialists, AI retrieval practitioners, SEO professionals, and data-quality experts are invited to review the specification, test implementation, and contribute feedback through the EntityMap community forum and GitHub repository.
Fred Laurent, CTO of InLinks and Waikay, said: “Where a sitemap tells search engines which pages exist on a website, EntityMap tells AI systems what an organisation is, what it does and how its knowledge connects.” Laurent emphasized that AI systems are increasingly asked to summarize, recommend, and explain organizations, but if the underlying information is fragmented, machines are forced to infer relationships. “EntityMap gives them a structured source of truth to work from,” he added.
The need for EntityMap arises as AI systems are now used to answer questions that would historically have been asked through search engines, websites, or customer-service teams. Yet organizations have limited control over how those systems interpret their websites. A company's products, services, expertise, locations, leadership, accreditations, and relationships may be spread across many pages, and AI systems often retrieve small fragments, reconstructing meaning probabilistically. That can lead to incomplete answers or inaccurate representations.
EntityMap addresses this by allowing organizations to publish a single structured file that declares key entities, defines relationships, and links each claim back to its source evidence. The file can be reviewed by humans before publication, then read by machines in a consistent format.
Dixon Jones, co-founder of Waikay, said: “The web was built around pages, links and prose. AI retrieval needs a clearer layer of meaning and evidence. EntityMap is designed to help organisations say: these are the things we know, these are the relationships between them, and this is the evidence that supports those claims.”
EntityMap is published as a structured file at a predictable location on a website. It identifies important entities such as products, services, people, topics, locations, claims, or areas of expertise. It then maps relationships between those entities and links them to supporting pages. The project includes a specification, documentation, examples, and validation tools, and is published under CC BY 4.0 with no subscription or vendor lock-in.
The project has garnered attention from R.V. Guha, one of the founders of Schema.org, who said: “This is a good thing for the world.” The first phase of consultation focuses on technical review and early implementation, with wider adoption to follow.
EntityMap is relevant to any organization that needs AI systems to understand its information accurately. Potential use cases include healthcare organizations publishing accurate service information, financial services firms clarifying products and risks, legal and professional-services firms with complex expertise, publishers wanting clearer attribution, and brands concerned about AI misrepresentation. The standard is not designed to replace existing web standards but to add a structured evidence layer for AI systems.
Participants can review the specification at entitymap.org/spec/v1.0 and join the discussion at github.com/entitymap. The consultation runs until 30 June 2026.
