Search now revolves around entities—brands, people, products, places, and concepts—not just keywords.

If you do not define your entities clearly, AI assistants and Google will do it for you, often with messy results.

This playbook gives you a repeatable entity SEO framework: discover entities, model them into a graph, express them in content and schema, propagate them across the web, and measure impact on AI citations and revenue.

Pair it with our entity pillar at Entity Optimization: The Complete Guide & Playbook and our semantic strategy pillar at Semantic SEO that Scales: Entity-Led Strategy & KPIs to keep your graph aligned with content and navigation.

Why entity SEO beats keyword-only tactics

  • Disambiguation: assistants know which “Atlas” or “Nova” you are.

  • Coverage: entities travel across SERPs, Knowledge Panels, AI Overviews, and chat results.

  • Efficiency: one entity powers multiple pages and clusters instead of duplicative keyword pages.

  • Trust: explicit authors, organizations, and products strengthen E-E-A-T and reduce AI hallucinations.

The entity SEO lifecycle

  1. Discovery: inventory entities that matter for revenue and visibility.

  2. Modeling: design relationships and stable IDs.

  3. Expression: publish content, schema, and internal links that match the model.

  4. Propagation: align external profiles, PR, and partner mentions.

  5. Measurement: track coverage, citations, and business impact.

  6. Governance: keep the graph fresh as products and people change.

Discovery: find entities that move the needle

  • Mine SERPs, customer language, support tickets, and reviews for recurring nouns and concepts.

  • Extract entities with NLP (Google NLP API, spaCy) and cluster by theme: brand, products, features, problems, industries, locations, people, competitors, partners.

  • Identify ambiguity: same names across markets, overlapping product lines, or authors who share names with others.

  • Prioritize by revenue impact, search demand, and AI citation potential. Start with brand, top products/services, authors, and primary locations.

Modeling: design your entity graph

  • Assign stable @id URLs (e.g., /products/widget-2000#product, /team/ana-silva#person).

  • Define relationships: Organization owns Products, employs Persons, operates LocalBusiness locations, hosts Events, publishes Articles.

  • Add attributes: descriptions, images, identifiers (GTIN/ISBN), dates, locations, credentials, and sameAs sources.

  • Plan inheritance and reuse: one Organization reused across domains, one Person per author across languages.

  • Store the model in a repo or CMS to keep engineering, content, and PR aligned.

Expression: make entities machine- and human-readable

Content and UX

  • Create dedicated pages for core entities (brand, product, feature, author, location, event).

  • Lead each page with a plain-language definition and key facts; assistants quote these.

  • Use tables for specs, credentials, and timelines; keep numbers current.

Internal linking

  • Build pillar/cluster structures: pillars for core entities, clusters for supporting facets (use cases, industries, integrations, pricing, FAQs).

  • Use descriptive anchors that repeat canonical entity names plus context (industry, city) for disambiguation.

  • Add related links between sibling entities (product to feature, author to articles, location to events).

Structured data

  • Mark entities with JSON-LD: Organization, Person, Product/Service, LocalBusiness, Event, Article/BlogPosting, FAQ, HowTo.

  • Connect them with @id references: author → Article, brand → Product, organizer/location → Event, publisher → Article.

  • Add about and mentions to pages to signal primary and secondary entities.

  • Keep schema aligned with visible content; no phantom entities.

Propagation: align off-site signals

  • sameAs: link to authoritative profiles (LinkedIn, Crunchbase, GitHub, Wikipedia/Wikidata where appropriate). Avoid low-trust or stale profiles.

  • PR and digital mentions: secure coverage that repeats canonical names and context (industry, city). Link back to entity pages.

  • Partner and integration pages: co-create content that references shared entities with stable IDs.

  • Listings: for LocalBusiness, keep NAP and hours consistent across GBP, Apple Maps, and major directories.

Measurement: track entity health and impact

  • Coverage: number of target entities with live pages, schema, and sameAs links.

  • Eligibility: rich result detection for Article, Product, LocalBusiness, Event, FAQ/HowTo.

  • AI citations: mentions of brand, products, authors, and locations in AI Overviews and assistants; log examples and prompts used.

  • CTR and conversion: compare pages with complete entity schema vs those without; segment by template.

  • Disambiguation success: reduced use of modifiers in branded queries (e.g., fewer “brand + city” refinements) and cleaner Knowledge Panels.

  • Freshness: days since last update for bios, prices, hours, and events.

Tools and workflows

  • Extraction: NLP libraries or tools like InLinks/WordLift for entity suggestions.

  • Modeling: simple spreadsheets or graph tools; store IDs, descriptions, sameAs, owners, and last updated.

  • Implementation: CMS fields for IDs and sameAs; JSON-LD templates; components that enforce required fields.

  • Validation: Rich Results Test, Schema Markup Validator, and crawlers with extraction to confirm presence and parity.

  • Monitoring: dashboards for coverage, eligibility, AI citations, and traffic; alerts for schema errors or missing IDs.

Page-type blueprints

Homepage or About page

  • Main entity: Organization. Include description, logo, founding info, address (if relevant), and sameAs.

  • Mentions: top products/services, locations, founders.

  • Schema: Organization with about/mentions, plus WebSite searchbox if applicable.

Product or Service page

  • Main entity: Product or Service with offers and identifiers.

  • Mentions: use cases, industries, compatible systems.

  • Schema: Product/Service linked to Organization; related HowTo/Video for usage.

Author page

  • Main entity: Person with jobTitle, worksFor, credentials, image, sameAs.

  • Mentions: specialties, publications, events.

  • Schema: Person; link authored and reviewed Articles to this ID.

Location page

  • Main entity: LocalBusiness with NAP, geo, hours, priceRange.

  • Mentions: services, nearby areas served, events hosted.

  • Schema: LocalBusiness linked to Organization; Events organized at the location.

Article/Guide

  • Main entity: Article with about/mentions pointing to the primary entities covered.

  • Mentions: products, places, people referenced.

  • Schema: Article with author Person, publisher Organization, FAQ or HowTo where applicable.

Disambiguation tactics

  • Add industry or geography in headings and descriptions (“AISO Hub, AI search agency in Lisbon”).

  • Use consistent naming across site and off-site profiles; avoid nicknames or internal shorthand on public pages.

  • Provide concise definitions in the first 50–75 words of each entity page.

  • Include image alt text that repeats canonical names and context.

  • Redirect duplicate pages; maintain one @id per entity across languages and domains.

Multilingual and multi-market execution

  • One @id per entity across languages; translate name and description, not IDs.

  • Use inLanguage in schema and align with hreflang tags.

  • Localize addresses, currencies, and timezones for LocalBusiness and Event; keep ISO formats in schema.

  • Monitor AI citations by market; fix locales with weak representation.

Governance and roles

  • Owners: content/SEO for requirements and monitoring; engineering for templates; data/ops for feeds; PR for sameAs quality.

  • Standards: documented @id rules, sameAs sources, required fields per template, and review cadences.

  • CI: lint for required fields, @id presence, and rendered HTML checks to catch missing schema.

  • Change log: record schema releases, rebrands, and content overhauls to trace impact.

Rollout plan (90 days)

  • Weeks 1–2: Entity audit; prioritize brand, products/services, authors, locations. Define @id map and sameAs policy.

  • Weeks 3–4: Ship dedicated pages and base schema for top entities; validate in staging; clean up duplicate IDs.

  • Weeks 5–6: Build dashboards for coverage, eligibility, and AI citations; start prompt testing.

  • Weeks 7–9: Expand schema to remaining templates; add about/mentions; connect internal links to mirror the graph.

  • Weeks 10–12: Propagate off-site (profiles, PR, directories); formalize governance rituals and CI checks.

Prompt testing for AI visibility

  • Keep a bank of prompts per entity: who/what/where/price/availability/credentials.

  • Test monthly in AI Overviews, Perplexity, and Copilot; capture outputs and sources.

  • When answers omit you or cite competitors, improve definitions, sameAs, and on-page clarity; retest after changes.

  • Track wins and misses over time to show progress and guide content updates.

KPI targets

  • Coverage: >90% of priority entities have live pages, schema, and sameAs.

  • Eligibility: zero blocking errors on core templates; warnings resolved within two sprints.

  • AI citations: month-over-month growth; investigate drops >20%.

  • CTR lift: 5–15% improvement on pages after adding complete schema and disambiguation.

  • Freshness: bios updated at least yearly; prices/hours within 24 hours of change.

Case scenario: SaaS team clarifies a crowded brand name

Issue: brand name overlaps with a popular open-source project.

Actions:

  • Added industry and location to homepage definition and Organization schema.

  • Strengthened Person pages for founders with sameAs links to LinkedIn and conference talks.

  • Linked products and integrations explicitly via isRelatedTo; added FAQs clarifying use cases.

  • Result: branded queries required fewer modifiers, AI Overviews began citing the SaaS brand, and CTR on branded terms rose 9%.

Case scenario: clinic network improves entity clarity

Issue: multiple clinics shared practitioner names, causing mixed reviews and hours.

Actions:

  • Assigned unique Person @id per practitioner; added specialties and sameAs (medical boards, LinkedIn).

  • Linked each Person to the correct LocalBusiness location; added Events for workshops.

  • Synced hours and addresses from a central system; removed duplicate location pages.

  • Result: Knowledge Panels stabilized, local pack results matched real hours, and AI assistants recommended the right clinic.

Content ops integration

  • Add entity requirements to every brief: primary entity, supporting entities, @id, pillar link.

  • During editing, verify the first paragraph defines the primary entity and that schema matches the copy.

  • After publishing, validate schema and run prompt tests for the primary entity.

  • Log changes to entity pages and re-run crawls when bios, prices, or hours change.

Governance cadences

  • Weekly: review errors/warnings, prompt test results, and AI citations for top entities.

  • Monthly: refresh bios and images that changed; crawl for missing @id or sameAs.

  • Quarterly: audit the ID map, deprecate obsolete entities, and retrain teams on standards.

Dashboards and alerting that keep teams aligned

  • Inventory: list every target entity with status (page live, schema live, sameAs live, last updated) and owner.
  • Coverage: percentage of pages per template with required schema; alerts when coverage dips below thresholds.
  • Citations: AI Overview and assistant mentions by entity; links to example prompts and outputs.
  • Impact: CTR, conversions, and leads tied to entity pages before/after schema or content changes; annotate releases.
  • Freshness: age of bios, prices, hours, and events; highlight stale records that risk AI trust.

Maturity model for entity SEO

  • Baseline: key entities identified, Organization and Person schema live, basic pillar/cluster structure.
  • Structured: stable @id map, Product/Service and LocalBusiness schema deployed, sameAs standardized, internal links mirror the graph.
  • AI-ready: about/mentions deployed, prompt testing routine, dashboards track AI citations, multilingual IDs aligned.
  • Optimized: experiments on new schema types (Clip, Speakable), entity-driven briefs, and automated alerts for drift.
  • Adaptive: rapid updates after launches or rebrands, entity experiments tied to revenue, and governance council prioritizes work quarterly.

Building a lightweight knowledge graph

  • Store entities, relationships, and IDs in a spreadsheet or graph tool; include sources and evidence for claims.
  • Use about, mentions, and sameAs to express relationships in JSON-LD, effectively publishing a mini knowledge graph on your site.
  • Add subjectOf or mainEntityOfPage where relevant to connect content assets to entities.
  • For integrations or partner ecosystems, use isRelatedTo or compatibleWith to make relationships explicit.
  • Keep the graph synchronized with navigation and breadcrumbs so crawlers see the same structure in links and schema.

Localization and EU/Portugal specifics

  • Disambiguate similar city names by including district or region in descriptions and schema.
  • Use Portuguese labels alongside English when targeting both; keep one @id to consolidate authority.
  • For regulated sectors (health, finance), surface reviewer or medicalSpecialty attributes and on-page credentials to satisfy trust needs.
  • Respect GDPR when linking sameAs profiles; avoid personal accounts without consent and provide contact paths via Organization contactPoint.

Handling rebrands and expansions

  • Keep @id values stable; redirect URLs and update canonical links but do not mint new IDs for existing entities.
  • Update sameAs links across all profiles; announce changes via PR to reinforce new naming.
  • Refresh definitions, logos, and images in schema and on-page; rerun prompt tests to confirm assistants adopt the new language.
  • Monitor branded queries and AI citations weekly for three months post-rebrand; adjust disambiguation copy if confusion persists.

Integration with analytics and CDP

  • Tag entity pages with IDs in analytics (custom dimensions) to aggregate performance by entity type.
  • Feed clean entity data into your CDP to personalize experiences and measure downstream revenue tied to specific entities (e.g., product lines or locations).
  • Join Search Console data with the ID map to spot entities with impressions but no citations or rich results.

Testing and QA checklist

  • @id present and stable for all core entities on the page.
  • Organization and Person schema linked correctly; author/reviewer visible on-page.
  • about/mentions contain 1–3 primary entities plus a small set of supporting entities that appear on-page.
  • Rich Results Test passes for the template; no blocking errors.
  • sameAs links resolve and point to authoritative profiles.
  • Internal links use canonical names and point to the right entity pages.
  • Prompt tests show accurate answers for the entity in AI Overviews or assistants; gaps logged.

How AISO Hub can help

AISO Hub builds entity-first search programs that AI assistants and Google trust.

We map your entities, design the graph, ship JSON-LD templates, and align off-site signals.

  • AISO Audit: find ambiguity, gaps, and schema errors with a prioritized graph roadmap

  • AISO Foundation: deploy the graph, templates, and governance across templates and languages

  • AISO Optimize: expand clusters, earn citations, and test new schema types tied to conversions

  • AISO Monitor: track coverage, eligibility, and AI mentions with alerts and exec-ready reporting

Conclusion: control your narrative

Entity SEO lets you control how search engines and AI describe your brand.

Define entities with stable IDs, express them in content and schema, propagate them across the web, and monitor performance.

When you run this playbook, you reduce ambiguity, earn more citations, and turn entity clarity into measurable growth.