From Mentions to Machines: How Entity Mapping, Schema, and Citation Are Rewriting Brand Visibility

The age of search is ending; the age of synthesis has begun.
Today, brand visibility no longer depends on ranking in Google — it depends on being understood and trusted by large language models like ChatGPT, Gemini, and Perplexity. These systems don’t index pages; they interpret entities, relationships, and sources of truth.

This book, From Mentions to Machines, reveals how organizations can structure their presence for AI comprehension and citation. It introduces a three-stage framework — Entity Mapping → Schema Recommendation → Citation Opportunity — that transforms traditional SEO and PR practices into the architecture of machine trust.

Entity Mapping defines what your brand is across the web’s structured ecosystems, ensuring consistency in how people, products, and organizations are represented in data. Schema Recommendation translates that understanding into markup — the technical language AI systems use to retrieve, rank, and attribute knowledge. Citation Opportunity converts visibility into authority by engineering credible, licensable references that generative AI systems can safely quote.

Through case studies and practical templates, the book explains how publishers can future-proof their journalism for AI citation, how content teams can plan around entities instead of keywords, how PR firms can structure credibility for machine retrieval, and how SEO teams can evolve from ranking optimization to knowledge graph engineering.

Blending editorial strategy, data structure, and digital communications, From Mentions to Machines is the definitive guide to AI Visibility — the discipline of making brands discoverable, citable, and trusted in an era where machines decide what the world sees and believes.

The New Rules of AI Visibility

The Shift from Search to Synthesis

For two decades, search revolved around keywords — users typed, algorithms ranked, and brands optimized. But in the new era of generative discovery, people no longer search; they ask. AI assistants like ChatGPT, Gemini, Copilot, and Perplexity respond not with blue links, but with synthesized answers.

This shift changes everything about visibility. Instead of competing for a search result, brands now compete for inclusion in the model’s answer. Large language models (LLMs) select what to surface based on structured understanding, data credibility, and context alignment — not metadata tricks or backlinks.

For marketers and communicators, visibility now means being machine-readable, semantically defined, and citable within the data these systems learn from. In this landscape, trust and structure outweigh keywords and clicks. The question is no longer “How do we rank?” but “Are we understood?”

What AI Visibility Really Means

AI Visibility is a brand’s ability to be discovered, cited, and trusted by machine intelligence. It’s not visibility to a human reader scrolling through Google — it’s visibility to an AI model choosing what information to retrieve, summarize, or attribute.

Three pillars underpin AI Visibility:

Discoverable — The brand’s data, people, and products are machine-locatable through structured markup and defined entities.
Citable — The brand’s content and coverage are licensable, verifiable, and usable as evidence within AI outputs.
Trusted — The brand’s reputation, sources, and transparency earn inclusion within authoritative responses.

When these three align, a brand becomes AI-fluent — seamlessly present across conversational systems, recommendation engines, and training data pipelines.

The Framework: Entity → Schema → Citation

Every brand’s digital presence now follows a three-stage journey from truth to trust:

Entity Mapping defines the facts: what the brand is, who it represents, and how its products, people, and ideas connect to the wider web of knowledge.
Schema Recommendation encodes that truth into structured, machine-readable markup so AI systems can interpret and retrieve it.
Citation Opportunity transforms structure into authority — ensuring that credible media, wikis, and databases reference and license that information so it can be quoted and credited in AI-generated answers.

Together, these stages form the backbone of AI Visibility Engineering — a discipline uniting SEO precision, PR credibility, and data science clarity into one operating model.

The Machine Legibility Imperative

Search engines were built to crawl; AI systems are built to comprehend. The difference is profound.
A crawler follows links and indexes text. A model reads context, extracts entities, and calculates trust. If your brand’s data isn’t structured — if your articles, bios, and product descriptions lack schema, metadata, or relational context — AI assistants simply can’t “see” you.

Machine legibility is the new readability. It means designing your web presence for machines to understand at a conceptual level: who you are, what you sell, and why you’re credible.
The same clarity that helps LLMs recognize your brand also improves discoverability in voice search, smart assistants, and marketplace recommendations.

Without structure, you’re invisible to the systems shaping modern discovery. With it, you become part of the knowledge graph that AI draws from to explain the world.

The AI Knowledge Supply Chain

Every piece of content now participates in an invisible supply chain — one that powers the world’s AI systems.

It begins with creation (the article, video, or dataset you publish), continues through indexing and training (where models ingest structured data), and culminates in retrieval and recommendation (where your information appears in an AI-generated response).

Understanding this supply chain means designing content not just for audiences, but for machines that mediate those audiences.

Publishers feed journalism into model training corpora.
PR firms shape the citations those models rely on.
Content teams produce explainer material that becomes reference data.
SEO professionals ensure the entire ecosystem is connected, structured, and discoverable.

The winners in this new landscape are not the loudest — but the most legible, linked, and licensed.
In the age of synthesis, visibility isn’t earned by keywords or volume, but by clarity, structure, and truth.

Entity Mapping: Defining the Truth

Building the Entity Graph

Every brand exists within a network of relationships — between people, products, partners, audiences, and ideas. Entity Mapping is the process of translating that network into a machine-readable graph.

An entity is any uniquely identifiable concept — a person, place, product, or organization — that AI systems can link to a verified reference. When those entities are consistently described across your website, social channels, Wikidata entries, and media coverage, they form your Entity Graph: the structured DNA of your brand.

How to build it:

Inventory your entities. Start with core identity (organization, founders, products, expertise).
Define relationships. Who is affiliated with what? Which entities depend on or reference others?
Align identifiers. Use consistent labels and sameAs links to connect profiles across the web.
Structure connections. Represent these links in your site architecture, metadata, and internal linking.

A coherent entity graph ensures AI systems understand who you are and how you relate to the world. Without it, your data remains fragmented — and your visibility scattered across unconnected mentions.

Canonical Identity Management

Every organization must now manage not only its brand image, but its machine identity — the canonical record of “who and what” it is across structured data ecosystems.

Platforms like Wikidata, Google Knowledge Panels, LinkedIn, and Crunchbase form the foundation of machine perception. They provide identifiers that models like ChatGPT use to validate facts.
If your brand’s entity definition is missing, inconsistent, or duplicated, AI systems may misattribute your data — or worse, ignore it entirely.

Canonical identity management means:

Claiming and maintaining Knowledge Panels and Wikidata entries.
Linking all official web properties through sameAs relationships.
Defining a single, authoritative Organization schema with unambiguous details (founding date, key people, sectors, mission).
Tracking external data references to ensure alignment across platforms.

Think of it as brand governance for machines: ensuring your digital truth remains coherent, verifiable, and retrievable — everywhere it matters.

Entity Mapping for Publications

For publishers, your content is already rich with entities — authors, topics, people, companies, events — but most of them exist as text, not data. To be visible in AI systems, those entities must be structured, linked, and consistent.

Key actions:

Assign unique IDs to every author, section, and topic within your CMS.
Link articles to their underlying entities (e.g., an article tagged “AI Ethics” connects to the Wikidata entity for Artificial Intelligence Ethics).
Build internal entity pages (like mini-Wikipedia entries) summarizing each recurring subject.
Maintain structured author profiles with verified Person schema and sameAs references.

By turning editorial relationships into structured relationships, publishers move from storytelling to knowledge modeling.
The result: articles that not only inform readers, but train machines — making your publication a trusted data source for LLMs and generative news systems.

Entity Mapping for PR Companies

Public relations has always been about managing perception — but in the AI era, perception is shaped by structured identity.
Entity Mapping for PR means defining your clients not just as brands, but as data-backed entities that AI systems can recognize, differentiate, and cite.

Core components:

Map organizational entities (brand, divisions, subsidiaries).
Define spokesperson entities with consistent bios, roles, and sameAs identifiers.
Structure product entities using schema (Product, Offer, Review) to enable AI comparison.
Ensure press releases use structured metadata so that coverage reinforces machine understanding.

PR firms that master entity mapping no longer just “earn coverage” — they engineer credibility. They ensure that when AI systems summarize an industry, their clients appear as verified participants, not as names lost in the narrative.

Entity Mapping for SEO Teams

SEO is shifting from optimizing for queries to optimizing for entities.
Search engines and AI assistants now rely on knowledge graphs, not just keywords, to determine context and relevance.
For SEO professionals, this means building sites that clearly define, interlink, and reinforce entities throughout their structure.

Tactical focus:

Use taxonomy and internal linking to express relationships between topics and pages.
Implement comprehensive Organization, Product, and FAQPage schemas.
Ensure content explicitly references recognized entities (e.g., via Wikidata IDs or structured context).
Consolidate duplicates — one entity per product, one canonical source per topic.

An entity-first website helps both Google and LLMs connect your brand’s content to verified truth. Instead of fighting for rankings, SEO teams become architects of semantic authority — designing the frameworks that make a brand’s knowledge machine-visible.

Entity Mapping for Content Teams

Traditional editorial planning revolves around keywords and calendar slots. In the AI age, it revolves around entities and clusters — connected ideas that define expertise.

Entity-driven content strategy means:

Planning topics around conceptual ecosystems (e.g., “sustainable packaging” → materials, suppliers, regulation, innovation).
Building pillar pages that anchor each entity and link to related subtopics.
Ensuring every piece of content connects to a clear parent entity, avoiding orphaned material.
Using structured metadata (tags, schema, internal linking) to create visible topic authority.

When content teams work from an entity map instead of a keyword list, they build depth over breadth — reinforcing the brand’s authority in the eyes of both humans and machines.

Entity mapping becomes the editorial foundation for AI-ready publishing: a system where every piece of content strengthens the brand’s position in the world’s interconnected web of meaning.

Schema Recommendation: Structuring for Machines

Schema as a Language of Trust

Search engines and AI systems speak a different language — one built not from prose, but from structured data. Schema.org, JSON-LD, and the emerging LLM.txt standard are the vocabularies through which human meaning becomes machine understanding.

Schema is the connective tissue of the semantic web. It describes entities, relationships, and context — ensuring that when an AI system encounters your content, it knows what it’s looking at and trusts what it sees.

Without schema, your content is a wall of text. With it, every component — an author, product, event, or quote — becomes a defined data object. This structure allows AI assistants to retrieve, rank, and even cite your material with confidence.

Why it matters:

Schema builds credibility through transparency.
JSON-LD makes your structure portable across the open web.
LLM.txt defines your content’s rights, purpose, and retrievability for large language models.

In the age of generative search, schema isn’t technical decoration — it’s brand integrity written in code. It tells machines: You can trust this source.

Editorial Schema for Publishers

Publishers sit at the heart of the information ecosystem — and are among the most dependent on structured data for visibility. Schema gives editorial content a second life: as training data, knowledge references, and AI citations.

Core markup for publishers:

Article and NewsArticle schema to define stories with precision — headline, author, date, publisher, section, and keywords.
Person schema for authors and contributors, with verified identifiers (sameAs links to LinkedIn, Wikidata, or official profiles).
Organization schema to identify the publication itself, including brand logo, URL, and ownership transparency.

Best practices for metadata consistency:

Standardize authorship across platforms — the same name, same ID.
Include publication and modification dates for transparency.
Use mainEntityOfPage to connect related stories and their topics.
Publish correction policies and licensing information in structured form.

The result is editorial clarity at scale — where every story reinforces the publication’s authority within AI systems’ knowledge graphs.

Schema for Content Operations

For most teams, schema fails not because it’s unimportant, but because it’s invisible to the editorial workflow. The solution is embedding schema management into content operations — making it part of the publishing lifecycle, not a post-launch task.

Operationalising schema means:

Integrating schema templates directly into the CMS (pre-filled fields for article type, topic, author, and related entities).
Including structured data checks in QA and editorial review.
Maintaining a schema library aligned to your taxonomy (e.g., templates for guides, FAQs, interviews, and product pages).
Training editors to think semantically — writing headlines and intros that express relationships, not just keywords.

When schema becomes part of the editorial muscle memory, every new piece of content contributes to the brand’s entity graph automatically.
Over time, this creates machine-consistent editorial intelligence — a content system that explains itself to AI as it grows.

Schema for PR and Communications

In communications, authority isn’t declared — it’s structured. Schema allows PR teams to codify credibility, provenance, and expertise.

Applications of schema in PR:

NewsArticle and PressRelease markup for every newsroom post or announcement.
Person schema for spokesperson bios and expert profiles, linking to affiliations and credentials.
Organization schema for corporate profiles and brand positioning pages.
Event schema for launches, summits, or campaigns.

Structured newsroom pages signal to AI systems that your content is official, current, and attributable. Pairing this with LLM.txt — specifying how your materials can be cited or summarised — ensures your coverage is AI-accessible and legally compliant.

In an environment where AI systems decide which voices to trust, schema is your brand’s passport to credibility. It transforms press materials from PR collateral into verified sources of record.

Schema for SEO Teams

Schema was once an SEO enhancement. It’s now an SEO foundation.
Where Google once ranked based on backlinks and text relevance, its algorithms — and those of other AI-powered systems — now interpret entities, context, and structure through schema.

Expanded schema strategy for SEO:

Go beyond product markup. Use FAQPage, HowTo, Service, and Organization to cover the full customer journey.
Add review and aggregate rating markup to highlight trust indicators.
Ensure cross-linking between schema entities (Person → Organization, Product → Offer).
Validate markup continuously using Search Console and LLM schema validators.

Schema transforms SEO from keyword optimization to context optimization.
The reward isn’t just better rankings — it’s inclusion in AI summaries, product comparisons, and voice-assistant responses.

As search evolves into conversation, structured data ensures your brand’s knowledge becomes part of the answers, not just the index.

Schema for Knowledge Transfer

Structured data doesn’t just power external visibility — it underpins internal intelligence too.
Schema is the backbone of Retrieval-Augmented Generation (RAG), the technology behind private GPTs, corporate assistants, and AI knowledge bases.

How it works:

Schema defines what each document, dataset, or record means.
RAG systems use this structure to retrieve the most relevant information and feed it into a language model.
The result: accurate, contextual AI responses that reflect verified internal knowledge.

Organizations that already use schema externally can repurpose it internally — creating unified taxonomies, metadata standards, and AI training frameworks.

Schema becomes the bridge between human knowledge and machine comprehension. It turns data chaos into discoverable intelligence, ensuring that whether an answer is generated for a customer, employee, or AI assistant, it is structured, sourced, and trustworthy.

PART IV — Citation Opportunity: Engineering Authority

From Mentions to Citations

In the traditional web, mentions were enough — a brand appeared in articles, was quoted by journalists, and surfaced through backlinks. But in the era of AI-driven discovery, visibility requires a higher standard: citation.

AI systems like ChatGPT, Gemini, and Perplexity are trained to retrieve and reference content they can trust, license, and verify. That means only sources with clear ownership, structured metadata, and transparent provenance are considered “safe” to include in generated answers.

Being mentioned is passive; being cited is engineered.
A citation tells a machine: This content can be referenced, attributed, and redistributed within AI responses without risk or ambiguity.

For brands and publishers, the evolution from mentions to citations marks a shift from reputation marketing to data legitimacy engineering.
In this new ecosystem, credibility is a function of structure, licensing, and consistency — not volume or virality.

How LLMs Decide What to Cite

Large language models evaluate sources through a hidden hierarchy of credibility — a mix of data transparency, authority, and accessibility.
Understanding this hierarchy is critical to designing a citation-ready brand.

The three core filters AI systems apply are:

Source Type: Trusted publications, government sites, academic repositories, and verified corporate domains are prioritized.
Transparency & Provenance: Structured metadata (schema, author, date, license) signals reliability. Anonymous or unverified pages are discounted.
Crawlability & Licensing: If content cannot be accessed, parsed, or cited under open terms, it is excluded from retrieval.

LLMs don’t “believe” sources — they weight them probabilistically. Structured data, verified identity, and citation-ready rights increase those weights.
In effect, credibility has become quantifiable. And the most visible brands in AI systems are those whose data architectures make trust machine-verifiable.

Citation Strategies for Publishers

For publishers, AI citation readiness is both an ethical duty and a competitive advantage.
As LLMs pull from media archives to answer questions, they prioritise sources with clear provenance, transparent metadata, and legal clarity.

How to strengthen citation readiness:

Ensure crawlability: Remove barriers like logins or paywall blocks from canonical article versions (while still protecting monetized content).
Clarify licensing: Publish clear terms under Creative Commons, open syndication, or AI-licensable agreements.
Structure ownership: Use Organization, Publisher, and NewsArticle schema to define authorship and copyright metadata.
Leverage Wikidata: Link your publication’s entity to recognized media databases and your own Wikidata item.
Embed provenance: Include named reviewers, fact-checking policies, and update timestamps in structured form.

A publisher who masters these principles doesn’t just earn human trust — they build machine-recognized authority, securing long-term inclusion in AI-generated news and educational outputs.

Citation Strategies for PR Firms

For PR companies, the goal is no longer just coverage, but citable coverage — material that becomes part of the reference layer informing AI models and search systems.

How to engineer AI-citable authority for clients:

Publish structured press materials: Each release should include NewsArticle or PressRelease schema, defining author, organization, and date.
Foster media partnerships with outlets that are AI-licensable and have crawlable archives.
Encourage expert attributions: Link quotes to structured Person schema with roles, credentials, and external identifiers (Wikidata, LinkedIn).
License intelligently: Use open or partial AI usage rights to make client content eligible for inclusion in LLM retrieval systems.
Align PR and SEO: Ensure every earned mention reinforces the client’s entity structure — linking back to verified profiles and data sources.

When PR campaigns are built with structured data and provenance in mind, your clients don’t just appear in headlines — they appear in AI summaries, answer boxes, and knowledge panels.
That’s the new measure of media influence.

Citation Strategies for SEO & Content Teams

In SEO, authority once came from backlinks; in AI visibility, it comes from citability.
The best-performing content isn’t merely optimized for keywords — it’s reference-grade: written, structured, and sourced in ways that AI systems can confidently reuse.

To make content citation-ready:

Embed source transparency — authors, update history, citations, and external references.
Publish structured metadata: FAQPage, HowTo, and WebPage schema with canonical URLs.
Reference primary data or external entities (Wikidata, industry databases).
Use plain-language definitions of terms to make explanations retrievable.
Add LLM.txt permissions specifying citation and summarization rights.

For SEO and content leaders, the aim is to create machine-referenceable assets — evergreen pages, guides, or insights that appear as sources of truth in generative search.
When your content becomes part of the model’s reasoning, you’ve transcended ranking — you’ve become infrastructure.

The Role of Wikidata and Wikipedia

In the AI ecosystem, Wikidata and Wikipedia have replaced backlinks as the ultimate trust signals.
They are structured, verified, and globally referenced — the very qualities that AI systems prize.

Why they matter:

Wikidata provides machine-readable entity definitions, giving models unique identifiers for people, brands, and concepts.
Wikipedia serves as the human-readable context layer that models reference for summarization.
Both feed into major AI datasets, ensuring recognized entities are understood, not misrepresented.

For brands, this means establishing and maintaining a verifiable presence on these platforms is now as critical as SEO once was.
Your entity’s inclusion in Wikidata confirms its existence; your Wikipedia profile anchors its narrative.
Together, they form the semantic foundation of public trust in the AI era.

Monitoring AI Mentions and Citations

The final step in engineering authority is measurement — tracking how and where AI systems reference your brand.
Unlike traditional media monitoring, AI citation tracking measures influence in machine reasoning rather than human perception.

Practical methods:

Use Perplexity and ChatGPT queries to identify when your brand is referenced in answers.
Track Wikipedia and Wikidata backlinks for AI ingestion points.
Set up semantic monitoring tools (e.g., LLM query scripts) to detect brand mentions across conversational outputs.
Integrate results into analytics dashboards to correlate visibility, sentiment, and source quality.

The goal isn’t to chase volume, but verify inclusion:
Is your brand cited in trusted contexts? Are AI systems referencing your official data, or secondary sources?

When monitored systematically, AI citations become the new proof of influence — evidence that your brand’s truth has been absorbed, trusted, and repeated by the machines shaping public knowledge.

Operationalizing AI Visibility

Integrating AI Visibility into Team Workflows

AI Visibility is not a marketing campaign or a technical experiment — it’s a cross-functional discipline.
Its success depends on aligning PR, SEO, content, and data teams around a shared goal: machine legibility.

Each team holds a piece of the equation:

PR controls the sources that shape credibility and citation potential.
SEO governs the technical structures that define how content is parsed.
Content and Editorial determine what information exists, how it’s written, and which entities it reinforces.
Analytics and Data measure retrievability, inclusion, and AI mentions.

Integration means:

Building shared taxonomies for topics, entities, and structured metadata.
Embedding schema and LLM.txt management into publishing and PR workflows.
Aligning campaign goals around being cited — not just being covered.
Creating joint performance reviews for human and machine visibility metrics.

When these functions converge under an AI Visibility roadmap, every new piece of content — whether a press release, article, or product page — becomes a data signal that machines can read, trust, and reference.

The Audit Stack

To operationalize AI Visibility, you must measure it. That’s where the Audit Stack comes in — a structured diagnostic system to assess how discoverable, legible, and citable your brand is across AI ecosystems.

The Audit Stack includes three tiers:

Entity Completeness Audit:
- Measures how clearly your brand, people, and products are defined across structured sources (website, Wikidata, LinkedIn, Google Knowledge Graph).
- Identifies inconsistencies, duplicates, or missing canonical identifiers.
Schema Quality Audit:
- Evaluates markup coverage, accuracy, and compliance with schema.org standards.
- Tests LLM.txt readiness and metadata depth (authorship, licensing, relationships).
Citation Depth Audit:
- Maps credible media coverage, Wikipedia/Wikidata references, and AI-visible licensing.
- Identifies which sources are most likely to be used or cited by generative systems.

Together, these audits reveal where your visibility breaks down — and where investment in structured truth will yield exponential return.

AI Visibility KPIs and Dashboards

You can’t improve what you can’t measure.
Traditional metrics like sessions and backlinks don’t capture a brand’s position in AI-driven discovery. New KPIs are required — ones that reflect inclusion, retrievability, and trust.

Key AI Visibility KPIs:

Retrievability Rate: How often your content appears in AI responses or summaries.
Entity Inclusion Score: Number of recognized entities (people, brands, products) represented in AI-visible databases.
Citation Frequency: Instances of your content or brand cited by AI models (via Perplexity, ChatGPT, or Gemini).
Trust Rank: Weighted credibility score based on source authority, structure, and provenance.
Machine Legibility Index: Composite score measuring schema quality, entity alignment, and LLM.txt completeness.

Dashboards should integrate:

Data from web crawlers, AI outputs, and schema validators.
Trend views of citation growth and knowledge graph completeness.
Cross-team attribution — linking PR coverage to entity recognition and SEO schema gains.

AI Visibility analytics are the connective tissue between communication strategy and data science — turning abstract credibility into quantifiable performance.

Case Studies: Media and Brand Leaders Who Got It Right

1. The Publisher:
A global news outlet integrated NewsArticle schema across its entire archive, added LLM.txt licensing metadata, and verified every author on Wikidata. Within six months, it became one of Perplexity’s top-cited media sources.

2. The Consumer Brand:
A luxury skincare company mapped its ingredients, formulations, and sustainability data as entities, then aligned them to Wikidata and its product schema. ChatGPT and Gemini began citing the brand in skincare routine recommendations — not through advertising, but as structured authority.

3. The PR Agency:
An agency embedded schema into client newsroom templates and ensured press releases linked to verified Wikidata entities. Its clients started appearing in AI summaries across technology and sustainability topics — demonstrating the power of data-driven credibility.

4. The SEO Team:
A retail platform migrated from keyword-driven content to entity-based clusters and schema-rich FAQs. Within 90 days, Perplexity and Bing Copilot responses began referencing its guides as the source of product comparisons.

Each success story shares one principle: machine legibility creates AI visibility. When your data is structured and your authority verifiable, AI systems can’t ignore you — they depend on you.

The Future of Generative Discovery

Generative AI is evolving from a retrieval tool into a knowledge operating system.
Tomorrow’s assistants will no longer just reference public content — they will curate, attribute, and explain using dynamic data graphs.

Emerging trends include:

Personal AI Curators: Assistants that rank brands based on structured transparency and data ethics.
Autonomous Attribution: Automatic crediting of sources via structured provenance tags.
The Rise of Structured Truth: Open, verifiable data networks replacing opaque search algorithms.
AI Licensing Markets: Publishers monetizing visibility through machine-readable rights metadata.
Conversational Commerce: E-commerce recommendations derived from verified product entities, not paid placements.

The future belongs to brands that treat data as narrative, schema as storytelling, and citations as currency.
In this world, trust is not claimed — it’s encoded.

Your AI Visibility Playbook

Implementing the Entity → Schema → Citation framework requires both strategy and precision. This final playbook provides a step-by-step pathway to make your organization machine-visible.

Step 1: Map Your Entities
List every person, product, and property that defines your brand. Align them with external identifiers (Wikidata, schema.org types, LinkedIn, Crunchbase).

Step 2: Structure with Schema
Embed consistent, validated schema across your site — Organization, Person, Article, Product, and FAQPage markup — and deploy an LLM.txt file for AI compliance and licensing.

Step 3: Engineer Citations
Pursue coverage in structured, licensable publications; contribute to Wikipedia; and publish transparent, sourced content that AI can safely reference.

Step 4: Audit and Measure
Run the AI Visibility Audit Stack quarterly to track retrievability, schema health, and citation depth.

Step 5: Integrate and Educate
Train cross-functional teams — PR, SEO, and editorial — to collaborate on entity governance and structured storytelling.

When executed consistently, this playbook doesn’t just future-proof visibility — it builds machine trust at scale.
Because in the age of AI discovery, visibility isn’t about being found — it’s about being understood, cited, and remembered.

Entity Mapping, Schema Recommendation, SEO, PR, Content, PublicationsFrancesca Tabor10 November 2025