Today a new free open standard aims to help organisations make their facts, relationships and evidence easier for AI systems to retrieve, understand and cite

EntityMap, the new open standard designed to help AI systems understand website knowledge more accurately, has entered a 33-day public consultation.

The project gives organisations 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 aim is to reduce the need for AI systems to infer meaning from fragmented web pages, making it easier for search engines, retrieval systems and large language model applications to access factual information directly from the source.

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.

“AI systems are increasingly being asked to summarise, recommend and explain organisations. If the underlying information is fragmented, incomplete or ambiguous, machines are forced to infer relationships. EntityMap gives them a structured source of truth to work from.”

Why EntityMap has been created

AI systems are now being used to answer questions that would historically have been asked through search engines, websites, professional advisers or customer-service teams.

Yet organisations 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. AI systems often retrieve small fragments of this content and reconstruct meaning probabilistically.

That can lead to incomplete answers, weak attribution or inaccurate representations of what an organisation does.

EntityMap has been developed to address this problem by allowing organisations 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 and a long-standing specialist in search, entities and AI visibility, 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.

“This consultation is about opening the standard up to scrutiny. We want people to test it, challenge it, implement it and help improve it before the formal launch.”

How it works

EntityMap is published as a structured file at a predictable location on a website. It identifies the important entities associated with an organisation, such as products, services, people, topics, locations, claims or areas of expertise.

It then maps the relationships between those entities and links them to supporting pages, allowing machines to retrieve an evidence-backed view of the organisation rather than relying only on isolated page fragments.

• a sitemap tells crawlers which pages exist
• schema helps describe what appears on a page
• EntityMap shows how the organisation’s key facts, entities and evidence connect across the site

The project includes a specification, documentation, examples and validation tools. It is published under CC BY 4.0, with no subscription, vendor lock-in or proprietary software requirement.

A consultation for the AI and web community

For example:

The 33-day consultation is intended to give the technical community time to review the structure, test practical implementation and identify improvements before the standard is finalised.

The project team is particularly seeking feedback from:

• developers and AI retrieval specialists
• structured-data and schema practitioners
• technical SEO professionals
• publishers and website owners
• data-quality and governance experts
• organisations concerned about AI misrepresentation
• tool builders interested in creating generators, validators or integrations

R.V. Guha, one of the founders of Schema.org, has reviewed the project and said: “This is a good thing for the world.”

The first phase of the consultation is focused on technical review, early implementation and community feedback. Wider adoption, sector-specific applications and further research into the standard’s potential impact will follow after the consultation period.

Who EntityMap is for

EntityMap is relevant to any organisation that needs AI systems to understand its information accurately.

Potential use cases include:

• healthcare organisations publishing accurate service, treatment or professional information
• financial services firms clarifying products, risks, advice boundaries and regulated information
• legal, professional-services and B2B organisations with complex expertise
• publishers that want clearer attribution for their knowledge and editorial content
• brands concerned about how AI systems describe their products, people or services
• technology teams building retrieval-augmented generation systems that need cleaner source data

The project is not designed to replace existing web standards. Instead, it is intended to add a structured evidence layer for AI systems that need to understand not just what pages exist, but what an organisation knows and how that knowledge connects.

How to take part

The EntityMap specification is available at:

entitymap.org/spec/v1.0

The community forum and source code repository are available 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.

For more information contact Dixon Jones at Email Contact

About EntityMap

EntityMap is a free, open standard that enables organisations to publish structured, machine-readable descriptions of their website knowledge for AI systems and retrieval tools. It allows organisations to define entities, map relationships and link claims to source evidence in a consistent format.

The standard is published under CC BY 4.0 and is open to community contribution. The public consultation runs until 30 June 2026, with official launch scheduled for 1 July 2026.

About Waikay

Waikay helps organisations understand and improve how they are represented in AI-driven discovery environments. Founded by specialists in search, entity optimisation and AI visibility, Waikay develops tools and frameworks for organisations navigating the shift from traditional search to AI-led retrieval and recommendation.

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Waikay, an innovation leader in AI brand visibility solutions, has announced the launch of their latest metric, Topical Presence, aimed at revolutionizing how brands measure their influence in AI-generated recommendations. Available immediately on Waikay.io, this new tool empowers brands to understand and optimize their presence across key topics within AI responses.

Unlike traditional search engines like Google that rank web pages, AI models such as ChatGPT and Gemini build associations with brands based on their presence in training data, which includes billions of web pages and community discussions. The AI Topical Presence metric measures how strongly and broadly these models associate a brand with relevant commercial topics, offering a new layer of insight into brand visibility.

Dixon Jones, CEO of Waikay explains, "Share of voice tells you how often your brand appears in AI responses. Topical Presence tells you what for. Waikay's Topical Presence maps which subjects AI associates with your brand, which it associates with your competitors, and where the gaps are. Traditional search analytics never gave you this. AI visibility measurement needs to."

The introduction of Topical Presence addresses a growing challenge for brands as they seek reliable and repeatable ways to measure their influence in AI tools like Claude and Gemini. By identifying both missing and displaced associations, Waikay's tool provides actionable insights for brands to enhance their AI visibility.

Waikay's Topical Presence uses a comprehensive scoring system based on depth, breadth, and concentration of topic associations. This allows brands to see not only where they are visible but also where they might be missing opportunities in the competitive landscape.

With Waikay's new offering, brands can now strategically enhance their AI visibility and ensure they are consistently part of the AI-generated conversations that matter most to their market. For detailed information, visit https://waikay.io/ai-topical-presence/.

About Waikay

Waikay is a brand of Inlinks Optimization LTD in the UK, a pioneering company in SEO tools and AI-driven brand visibility analytics, dedicated to helping brands navigate and succeed in the evolving digital landscape. They can be contacted via Inlinks.com.

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InLinks, the company behind the AI Brand Visibility platform Waikay.io, today released findings from a large-scale structural analysis of 5,000 websites, identifying 19,000 distinct gaps that are measurably reducing brand visibility across both traditional search engines and AI-powered platforms including ChatGPT, Perplexity, and Google SGE.

The research, one of the first to quantify the relationship between site architecture and AI search performance, found that more than half of all identified gaps (57%), fall into three categories: missing informational content (21.5%), absent product or service pages (18.5%), and UX or structural deficiencies (17.2%).

Why AI Search Changes the Stakes

Traditional SEO guidance has long addressed missing pages and poor site structure, but AI-powered search introduces a new layer of urgency. Platforms like ChatGPT and Perplexity synthesise responses from multiple sources, drawing on entity associations and content coverage rather than simple keyword matching. A website with structural gaps, missing topic clusters, orphaned pages, or thin category coverage, is more likely to be bypassed entirely.

“Businesses that have ignored structural issues may not have felt the consequences in traditional search yet, but in AI search, those gaps are immediate and significant,” said Dixon Jones, CEO of InLinks. “The sites that AI recommends are the ones that have done the work to clearly define what they cover, who they serve, and how their content connects. Gaps undermine all of that.”

Key Findings

• 57% of all identified gaps cluster into three root causes, suggesting that most websites share a common set of structural weaknesses rather than unique problems.

• Missing informational content (21.5%) is the single largest category. The absence of educational and explanatory pages that AI engines draw on to determine topical authority.

• UX and structural deficiencies (17.2%) affect crawlability and internal linking, limiting a site’s ability to signal the relationships between content. A critical factor for AI entity recognition.

• The severity and priority of gaps varies significantly by industry, competitive context, and customer journey stage. A one-size-fits-all remediation approach is unlikely to be effective.

Demonstrated Results

The report includes third-party case evidence alongside InLinks’ own testing. A major accounting software provider increased its AI entity associations for the term ‘e-invoicing’ by 650% following a programme of strategic internal linking, a change that required no new external links or paid media. InLinks separately validated the hub-and-cluster content methodology by improving its own AI recommendation ranking from 6th to 1st for a target category, providing a replicable framework for other organisations.

Methodology

The analysis was conducted using the Waikay.io platform, which audits websites against a structured taxonomy of gap types. The 5,000 sites were drawn from InLinks’ client and research database across multiple industries and geographies. Each gap was assessed against both traditional search signals and AI engine behaviour patterns observed between 2024 and 2025. The full methodology is published in the report.

The full report, including the gap taxonomy and prioritisation framework, is available at https://waikay.io/action-plans/seo-structural-gap-analysis/.

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