EntityMap: Open Standard Giving AI a Structured View of Your Business

AI systems are now fielding questions about your business, but too often, they deliver inaccurate answers. This isn’t a flaw in the models themselves; it’s a flaw in how information is structured for them.

Imagine a typical brand: its products, services, expertise, locations, leadership, and partnerships are scattered across dozens of web pages. An AI model pulls fragments from these pages, stitches them together based on probability, and generates a response. The result? Hallucinated product names, invented executives, misquoted capabilities, and a frustrating lack of attribution. The core problem isn’t the AI. It’s the medium. We’ve built the web around pages, links, and prose, but AI retrieval systems need something fundamentally different: a structured layer of meaning and evidence.

Enter EntityMap, a new open standard now in public consultation. It offers organizations a way to publish a single, structured file that declares what they know, maps how key entities relate to one another, and links every claim back to its original source evidence. The consultation runs until June 30, 2026, with a formal launch set for July 1. For the next 33 days, the project actively seeks implementation feedback, technical critique, and real-world testing from developers, SEO professionals, publishers, structured-data specialists, and anyone building or relying on AI retrieval systems.

Where EntityMap Fits in the Standards Landscape

EntityMap doesn’t replace existing web standards. It fills a gap that sitemap.xml and schema.org were never designed to address. Sitemap.xml tells crawlers which pages exist. Schema.org describes what appears on individual pages. EntityMap tells AI systems what an organization is, what it knows, and how that knowledge connects across the entire website.

This distinction is critical. Consider a healthcare organization publishing treatment protocols. With schema.org, you can annotate a single page. With EntityMap, you can declare: “Here are our core treatment areas. These are the relationships between them. Here is the peer-reviewed evidence supporting each claim. Here is where that evidence lives on our site.” An AI system reading that file gets a structured view of institutional knowledge rather than reconstructing it from page fragments.

Now consider a SaaS company worried about how AI systems describe its product. EntityMap allows the company to declare: “We offer feature X. It differs from competitors in Y. Here is the proof: link to documentation, link to case study, link to comparison page.” No longer must the company rely on an LLM to infer differentiation from scattered web content. The same logic applies to publishers protecting attribution, legal firms clarifying expertise boundaries, financial services firms navigating regulatory nuance, and brands concerned about AI misrepresentation.

How EntityMap Works

EntityMap is a JSON file published at a predictable location on a domain. It contains three core elements:

• Entities: Named things the organization covers, such as products, services, people, concepts, locations, regulations, and areas of expertise.Each chunk carries attribution metadata: the publisher name, the source page, and the retrieval timestamp. This metadata survives extraction, aggregation, and storage in vector databases. When an AI system generates a response using your content, the chain of evidence remains intact.The specification is deliberately minimal. The conformance floor consists of roughly 12 required fields across three objects. Everything else is optional enrichment: custom predicates, cross-shard resolution, verification status declarations, and changelog tracking.Who Should Pay Attention
• If you are building Retrieval Augmented Generation (RAG) systems, cleaner source data means better reasoning chains and fewer hallucinations.The standard is published under CC BY 4.0. There is no vendor lock-in, no subscription, and no proprietary software requirement. Community contribution is open. The source code, specification, and validation tools are all available at GitHub.What the Project Needs From YouThe consultation period is not ceremonial. The project team is actively seeking specific forms of feedback:
• Technical implementation feedback: Have you tried building an EntityMap for your site or product? What broke? What felt awkward in practice?The specification is available at entitymap.org/spec/v1.0. A validator is live at entitymap.org/validate. The community forum and GitHub repository are at github.com/entitymap. Participants are invited to review the specification, test implementation, raise issues, suggest improvements, and contribute to the discussion before June 30, 2026.Important Context: This Is Genuinely OpenThis is a standards proposal from within the search and AI community. R.V. Guha, one of the founders of schema.org, has reviewed the project and given it his endorsement. The consultation is genuinely open. The first phase focuses on technical review and early implementation. Wider adoption, sector-specific applications, and research into the standard’s broader impact will follow after the consultation closes.Why This Moment MattersIf you have spent the last few years watching AI systems misrepresent your work, your clients’ work, or your organization’s expertise, this is your moment to shape how that changes. The bar for entry is low. You need to review the specification, test it against a real problem you care about, and tell the project what you found. That feedback will inform the standard before it becomes finalized. The consultation runs for 33 days. After that, the adoption phase begins.Disclosure: I am the CEO of InLinks and Waikay, which both support the EntityMap standards proposal.