AI systems are now answering questions about your business. The problem is that they are often getting it wrong.
Consider the typical situation. A brand’s products, services, expertise, locations, leadership, and relationships are distributed across dozens of pages. An AI model retrieves fragments from those pages, stitches them together probabilistically, and generates an answer. The result is often hallucinated product names, invented executives, misquoted capabilities, and weak or absent attribution.
This is not a failure of AI models. It is a failure of the medium itself. We have built the web around pages, links, and prose. AI retrieval systems need something fundamentally different: a structured layer of meaning and evidence.
The Proposal: EntityMap
EntityMap has just entered public consultation. It is a new open standard that gives organizations a way to publish a single structured file. This file declares what the organization knows, maps how its key entities relate to one another, and links every claim back to its source evidence.
The consultation runs until 30 June 2026, with formal launch scheduled for July 1. For the next 33 days, the project is actively seeking 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 Sits In The Standards Landscape
EntityMap is not a replacement for 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 on a website. 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 matters. Consider a healthcare organization publishing treatment protocols. With schema.org, you can annotate a single page. With EntityMap, you can say the following: “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.
Or, consider a SaaS company concerned 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 are named things the organization covers: products, services, people, concepts, locations, regulations, areas of expertise.
Relations map how those entities connect. Examples: “this product improves this outcome,” “this person leads this team,” “this regulation governs this service.”
Evidence chunks are supporting passages from the website, linked to their source URL.
Each chunk carries attribution metadata: the publisher name, the source page, 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, 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.
If you are an SEO professional, this represents a new lever for AI visibility. It works with traditional content and link strategies rather than replacing them.
If you are a publisher, this is a way to declare what you know and preserve attribution as your content gets disaggregated across AI platforms.
If you are concerned about how AI systems represent your organization, this is a tool to assert control.
The standard is published under CC BY 4.0. There is no vendor lock-in, no subscription, 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 You
The 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?
Use-case validation: Does this solve a problem you actually face? Does it miss something critical to your domain or industry?
Predicate critique: The standard defines 24 core predicates (IMPROVES, DEPENDS_ON, MEASURES, and others). Are these the right semantic abstractions for your work? Should we add or remove from this list?
Integration ideas: Are you building a generator? A validator? A dashboard for managing EntityMaps? The project wants to know what tooling you are considering.
Sector-specific applications: If you work in healthcare, finance, education, legal, or another vertical, what would an EntityMap profile for your sector look like?
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 30 June 2026.
Important Context: This Is Genuinely Open
This 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 Matters
If 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.
More Resources:
- Structured Data’s Role In AI And AI Search Visibility
- Entities In SEO: What Are They And Why Do They Matter?
- How AI Chooses Which Brands To Recommend: From Relational Knowledge To Topical Presence
Featured Image: optimarc/Shutterstock
