Entity Optimisation for AI Agents

Understand how AI systems build entity models. AI systems construct entity models by aggregating signals from multiple sources: structured data (JSON-LD schema), content mentions (how your brand is described in text), cross-platform presence (LinkedIn, Wikipedia, Crunchbase, industry directories), and relational context (which other entities you are associated with). The entity model determines how AI systems represent your brand in generated outputs - inaccurate or incomplete entity models lead to misrepresentation or invisibility.

Recognise the three types of entities that matter for AI visibility. Organisation entities (your brand, subsidiaries, products), Person entities (founders, executives, thought leaders), and Concept entities (frameworks, methodologies, proprietary terms). Each entity type requires different optimisation strategies. Organisation entities need comprehensive structured data and cross-platform consistency. Person entities need credentials and expertise signals. Concept entities need definitional authority and consistent usage.

Map the entity optimisation landscape. Entity optimisation intersects with multiple disciplines: structured data implementation (Schema.org markup), knowledge graph management (Google Knowledge Panel, Wikidata), brand consistency (naming, description, visual identity), and content strategy (entity-consistent terminology). Effective entity optimisation requires coordination across all these areas - a structured data team implementing Organisation schema while the content team uses inconsistent naming will undermine both efforts.

Understand the entity authority hierarchy. AI systems evaluate entity authority through a hierarchy of signals: (1) structured data on your own website (strongest signal you control), (2) Wikipedia and Wikidata presence (highest third-party authority), (3) industry directory listings (domain-specific authority), (4) social media profiles (breadth of presence), (5) press mentions and citations (external validation). Optimise from the top of the hierarchy down - your own structured data is the foundation.

Establish the business case for entity optimisation. AI systems that cannot build a coherent entity model of your brand will not cite, recommend, or transact with you. In agentic commerce, where autonomous agents make purchasing decisions on behalf of humans, entity optimisation determines whether agents can identify your brand as a viable option. Entity optimisation is not a technical SEO tactic - it is a strategic imperative for AI-mediated commerce.

Implement Organisation schema as the entity foundation. Every page on your site should include Organisation schema with: name (canonical brand name), url, logo, foundingDate, founder (linked to Person schema), description (comprehensive, including key expertise areas), knowsAbout (array of specific topics), sameAs (array of all official profile URLs), contactPoint, and address. This Organisation schema is the single most important entity signal - it tells AI systems who you are, what you know, and where to verify your identity.

Implement Person schema for every key individual. For founders, executives, and thought leaders, implement Person schema with: name, jobTitle, worksFor (linked to Organisation), affiliation, sameAs (LinkedIn, Twitter, personal website, Wikipedia), knowsAbout (specific expertise areas), description (credentials and authority claims), and alumniOf. Person schema builds the expert entity models that AI systems use to evaluate content authority and attribute expertise.

Use knowsAbout properties strategically. The knowsAbout property in Organisation and Person schema tells AI systems what topics you are authoritative about. Be specific and comprehensive: not just 'artificial intelligence' but 'agentic commerce, agentic experience design, trust architecture, delegation design, human agent interaction, agentic shopping, machine customers.' The more specific your knowsAbout properties, the more precisely AI systems can match your entity to relevant queries.

Implement sameAs links comprehensively. The sameAs property connects your entity to its representations on other platforms, enabling AI systems to merge signals from multiple sources into a single entity model. Include sameAs links to: LinkedIn company page, Twitter/X profile, Wikipedia page (if it exists), Wikidata entry, Crunchbase profile, industry directory listings, YouTube channel, and any other official profiles. Missing sameAs links create fragmented entity models.

Maintain structured data consistency across all pages. AI systems evaluate entity consistency - do the Organisation and Person schema properties match across every page on your site? Inconsistencies (different founding dates, different descriptions, different sameAs links) reduce entity confidence. Implement structured data through a centralised configuration that ensures every page uses identical Organisation and Person schema. The AXD Institute uses shared schema generators that produce consistent structured data across all 178 pages.

Audit your current cross-platform entity consistency. Search for your brand name on: LinkedIn, Twitter/X, Wikipedia, Wikidata, Crunchbase, Google Knowledge Panel, industry directories, conference speaker pages, and any platform where your brand appears. Record the name, description, founding date, location, and key claims on each platform. Identify inconsistencies - different names, different descriptions, different dates. These inconsistencies fragment your entity model in AI systems.

Establish a canonical entity description. Write a single, authoritative description of your brand (2-3 sentences) that includes: founding date, location, founder, core expertise, and key differentiator. Use this exact description across all platforms - website, LinkedIn, Twitter, Crunchbase, industry directories. The more consistently this description appears across the web, the more confidently AI systems can build your entity model.

Prioritise Wikipedia and Wikidata for entity authority. Wikipedia and Wikidata are the highest-authority third-party entity sources for AI systems. If your brand is notable enough for a Wikipedia article, create one following Wikipedia's notability guidelines. If not, create a Wikidata entry (lower notability threshold) with: label, description, instance of, founded, founder, country, official website, and social media identifiers. Wikidata entries are directly consumed by many AI systems for entity resolution.

Build consistent entity signals in industry directories. Register your brand in relevant industry directories with consistent naming and description. For technology brands: Crunchbase, G2, Capterra. For professional services: industry-specific directories. For thought leadership: conference speaker databases, podcast guest databases, and expert directories. Each consistent listing reinforces your entity model in AI systems.

Monitor and correct entity drift. Entity representations drift over time as platforms update, content changes, and third parties create mentions with inconsistent information. Establish a quarterly entity audit: check all platforms for accuracy, correct any drift, and update descriptions to reflect current positioning. Entity optimisation is not a one-time project - it requires ongoing maintenance to prevent drift from undermining your AI visibility.

Use canonical terminology consistently across all content. If you define a concept (e.g., 'trust architecture'), use that exact term on every page, in every structured data block, and in every external mention. Do not alternate between 'trust architecture,' 'architecture of trust,' and 'trust framework' - AI systems may treat these as different concepts. Canonical terminology builds the concept entities that AI systems associate with your brand.

Include explicit entity statements in content. AI systems extract entity information from natural language content. Include explicit statements that reinforce your entity model: 'The AXD Institute, founded in September 2024 by Tony Wood in the United Kingdom, is the institutional home of Agentic Experience Design.' These statements, repeated across multiple pages, build the entity signals that AI systems use to construct your brand's knowledge graph entry.

Create dedicated entity pages. Create a dedicated page for your organisation (About page), each key person (bio/profile page), and each proprietary concept (definition page). These dedicated pages serve as canonical entity references that AI systems can use to build comprehensive entity models. Ensure each entity page includes comprehensive structured data and explicit authority claims.

Build entity relationships through content. AI systems understand entities through their relationships to other entities. Explicitly state relationships in your content: '[Person] is the founder of [Organisation].' '[Organisation] created [Framework].' '[Framework] is a component of [Discipline].' These relational statements help AI systems build the knowledge graph connections that determine how your entities are represented in generated outputs.

Publish entity-reinforcing content regularly. Each new piece of content is an opportunity to reinforce your entity model. Every essay, guide, and page should include consistent entity references: organisation name, founder name, key concepts, and founding claims. The cumulative effect of consistent entity reinforcement across hundreds of pages builds the deep entity authority that AI systems require to confidently cite and recommend your brand.