AI Visibility Platform for Product Releases
A release is not complete when your site changes. It is complete when the pages AI cites, the facts it repeats, and the recommendations it gives match the new product.
Author authority, engineered for answer engines
Authority Stack
Oscar Lindqvist turns E-E-A-T into an evidence architecture for teams that need their authors, experts, and claims to survive AI answer selection without relying on reputation folklore.
CORE / AUTHORITY / MACHINE-CITABLE
Machines do not admire expertise. They resolve it.
Answer systems look for a stable person, a traceable field of work, and evidence that a claim belongs to someone with the right history. Authority Stack treats that as an engineering surface: every byline, profile, credential, citation, and authored asset either adds load-bearing strength or creates a crack.
The evidence stack below a citeable author
- 01Entity fixityCan the machine tell this author from every similarly named person?
- 02Credential exposureAre qualifications visible where crawlers and users can confirm them?
- 03Topical continuityDoes the author have a pattern of work in the field, not a one-off claim?
- 04Corroborating mentionsDo third-party surfaces reinforce the same expertise identity?
- 05Claim accountabilityIs each commercial assertion attached to a qualified human source?
Featured load test
The authority problem is no longer only editorial.
A named author is not enough. Authority Stack breaks down the observable signals that make a person citeable to machines: consistent entity identity, credential traces, corroborated expertise, durable topical history, and proof that commercial claims are attached to someone qualified to make them.
Where authority collapses
Thin bios, orphaned expert pages, inconsistent role labels, unsupported credentials, anonymous review content, and vendor claims with no qualified speaker all make the same failure: they ask an answer engine to infer authority instead of reading it.
Core plugs
Recent authority core samples
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