Your founders are listed as people, not strings, with links to their public profiles
When AI is asked who started your company, can it cite a specific verifiable person?
What this signal tests
We check whether your homepage's Organization data lists your founders as proper Person entities, not as plain text. Each founder should have a name and ideally a sameAs or url linking to a disambiguating profile (LinkedIn, Wikidata, or ORCID). That linkage lets AI confirm which 'John Smith' actually founded the company.
Why it matters for your visibility in AI
"Who founded X?" is one of the most common questions asked of LLMs. When your founders appear in your structured data as full Person objects with sameAs links, the assistant can answer in one step: name, role, and a profile URL that proves identity. When founders are listed only as text strings, the assistant must guess from the page, and common names often produce wrong answers. Consider a user asking ChatGPT "who started Acme AI and what did they do before?" If your data says founder is the string 'Alex Johnson', the assistant may attribute the wrong career history (there are thousands of Alex Johnsons online). If your data says founder is a Person object with sameAs pointing to a specific LinkedIn or Wikidata entry, the assistant gets the right person every time and can pull in the right prior experience.
Pass criteria at a glance
| Criterion | Passes when |
|---|---|
| >=1 founder Person with name; full pass if each has sameAs. |
How we test it
We parse the Organization JSON-LD on your homepage and inspect the founder property. We require it to be either a single Person object or an array of Person objects (not a plain string). Each Person must have a name field. For a full pass, each Person also needs a sameAs URL or a url field pointing to a profile that uniquely identifies the individual (LinkedIn, Wikidata, ORCID, or a personal website). Plain-string founders fail the signal.
Show technical detection method
Organization.founder is Person object (or array) with name; bonus for sameAs (Wikidata, LinkedIn) or identifier (ORCID).
If your site fails: how to fix it
- Replace any string founder values in your JSON-LD with an object like { "@type": "Person", "name": "Alex Johnson" }.
- Add a sameAs array to each founder with at least one disambiguating profile URL: their LinkedIn (linkedin.com/in/...), their Wikidata Q-number if they have one, or their ORCID if they are a researcher.
- If multiple founders, wrap them in an array under the founder property; do not list only one when there were more.
- Optionally add jobTitle or alumniOf for context that helps AI summarize each founder's background accurately.
- Validate with Schema.org Validator and re-run the scan.
Quick facts
| Maturity | ESTABLISHED |
|---|---|
| Weight | medium |
| Category | Entity |
Primary sources
Related signals
Frequently asked questions
What if my company has many founders, do I list all of them?
Yes, list everyone who is publicly identified as a founder. Omitting co-founders is a common source of disputes and AI hallucinations; once one is on Wikipedia and another is on your site, assistants get confused about attribution.
What if I am a solo founder?
Still list yourself as a Person object with a sameAs link to your LinkedIn or personal site. A solo founder benefits even more from this signal because so much of the brand identity flows through your individual reputation.
Can I link to my founders' Twitter accounts?
Yes, X/Twitter URLs are valid sameAs values. Combine them with at least one higher-authority profile (LinkedIn, Wikidata, or ORCID) for stronger disambiguation.
Does this work for nonprofits or solo authors?
Yes, with slight terminology differences. Nonprofits typically use founder or member; authors can model themselves as a Person with knowsAbout topics. The structured-entity principle is the same: name the person and link to a disambiguating profile.
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