How to Establish Authorship and Organisational Identity

Why it matters

Training pipelines and answer engines assess credibility partly from identity: is there a real, named author with relevant expertise, an organisation that can be disambiguated, and consistent signals across the web? Anonymous content — no author, no verified profile, no About page — gives a reader, and a system weighing sources, nothing to assess. This is the structural backbone of what Google calls E-E-A-T.

Three components, all required: a named author bio, a verified profile linked by sameAs, and a complete organisational About page. Together they let an AI system resolve "who wrote this and can they be trusted?" with confidence.

What good looks like

Authorship and organisation are explicit, verifiable, and consistent:

• Every page has a named author bio — name, title, organisation, and domain expertise. "Christopher Foster-McBride, Founder, AI Knowledge Signal."
• A verified LinkedIn profile is linked, with sameAs schema connecting site → LinkedIn.
• A complete About page states legal name, location, services, and contact — consistent across all platforms.
• Identity is encoded in Person and Organization schema (Phase 5.1).
• Names, titles, and details match everywhere the organisation appears.

Step by step

1. Add a named author bio with credentials Attribute each page to a real person and state their name, title, organisation, and relevant expertise. Avoid "Admin" or "The Team" — a model can't build trust around an anonymous byline.
2. Link a verified LinkedIn profile via sameAs Link the author's verified LinkedIn profile and connect it with sameAs in Person schema. This is what ties the on-page name to an external, verifiable identity (Phase 4.3).
3. Publish a complete About page Create a full About page with the organisation's legal name, location, services, and contact information. This is the canonical organisational identity the rest of your entity graph points to.
4. Keep identity consistent across platforms Use the same legal name, author names, titles, and contact details on the site, LinkedIn, Crunchbase, and anywhere else. Inconsistency forces a model to guess whether two profiles are the same entity.
5. Encode it all in Person and Organization schema Mirror the visible identity in JSON-LD: a Person for the author (with sameAs) and an Organization for the publisher (with sameAs to LinkedIn/Crunchbase/Wikidata). The schema makes the identity machine-readable.

Also worth doing

• An author page per person, marked up as ProfilePage with a Person that every article’s author references by @id.
• For B2B, make the author’s own LinkedIn profile a publishing surface. LinkedIn’s October 2026 guide reports that about 75% of LinkedIn citations in AI answers come from individual member profiles and 25% from Company Pages (Meltwater, vendor-reported). Keep the profile public, consistent with the site bio, and linked both ways. Source →
• ORCID for authors who publish research, added to sameAs.
• Disclose AI assistance where content was drafted or edited with AI, and say who reviewed it. Google’s guidance asks “how was the content created?”

Worked examples

{
  "@context": "https://schema.org",
  "@type": "Person",
  "name": "Christopher Foster-McBride",
  "jobTitle": "Founder",
  "worksFor": {
    "@type": "Organization",
    "name": "AI Knowledge Signal",
    "url": "https://aiknowledgesignal.io",
    "sameAs": ["https://www.linkedin.com/company/your-company"]
  },
  "sameAs": ["https://www.linkedin.com/in/christopher-foster-mcbride-42234330/"]
}

Common mistakes