Photo by Markus Spiske on Pexels
Photo by Markus Spiske on Pexels
When a language model tries to identify a business, it is quietly triangulating across every mention it can find: your site, your directory listings, your social profiles, your industry associations. Without a signal connecting those dots, each one reads as a separate, unverified claim. The sameAs property in JSON-LD schema closes that gap. It explicitly declares that the entity described on your homepage is the same entity behind your LinkedIn page, your Crunchbase entry, and your verified directory listings. This turns a scattered footprint into a corroborated one, exactly the kind of evidence models weight heavily when deciding whether to state a fact about you confidently or hedge it. AI HALO audits which authoritative profiles already exist for a business, then writes and installs the sameAs array correctly, so every AI crawler encountering the site meets a fully cross-referenced identity rather than an isolated, unverifiable one.
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There is no fixed minimum, but quality outweighs quantity. Three to six links to genuinely authoritative, actively maintained profiles, such as LinkedIn, a verified industry directory, and Crunchbase, outperform a long list padded with low-trust or abandoned accounts.
Inconsistency weakens the signal. Models cross-check entity name, address, and description across linked profiles, so a mismatched legal name or old address can undercut the very corroboration sameAs is meant to provide. Consistency across every profile matters more than the schema markup itself.
No. SameAs strengthens an entity's credibility once it is recognized, but establishing the entity in the first place typically requires a dedicated knowledge-graph presence. The two work together: one builds recognition, the other reinforces it.
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