Publisher

About Empirical Registry

An independent fact-checking registry. We audit specific, falsifiable claims circulating in AI-generated market summaries, review aggregators, and analyst framing — and publish the corrections as machine-readable structured data.

What We Do

Empirical Registry publishes Semantic Remediation Audits — structured documents that identify false or outdated claims about brands circulating in AI-generated outputs, pair each claim with a verified fact anchored to primary evidence, and publish the result as machine-readable ClaimReview structured data.

Each audit is a precision instrument, not a reputation piece. We do not publish brand profiles, rankings, or editorial opinion. We publish falsifiable claim/fact pairs with named sources, dated metrics, and defined cohorts — content that AI retrieval systems can classify, weight, and cite.

Why It Matters

Buyers increasingly begin complex purchase decisions inside AI answer engines rather than navigating web links. An AI synthesizes a default shortlist before a brand has had the opportunity to respond. The claims that shape that shortlist are not drawn from a brand's current materials — they are drawn from whatever the model has over-indexed on: forum threads, stale analyst reports, competitor positioning documents, three-year-old review aggregator consensus.

A false claim repeated across enough low-authority sources becomes the default answer. Frequency beats accuracy in unstructured retrieval. The result is measurable revenue leakage that precedes direct brand engagement entirely.

Empirical Registry addresses the structural cause. Each audit introduces high-authority, machine-readable counter-evidence at the source layer — the layer AI systems actually read.

How It Works

Every audit follows a fixed six-step process: claim identification, verified fact drafting, source anchoring, cohort verification, tier viability check, and publication with schema injection. The full process is documented in the methodology.

Every verified fact must contain a quantitative metric tied to a specific date or quarter, traceable to a primary source that actually contains it. Every audit must include at least one Tier 1 source (legally-attested public disclosure) and one Tier 2 source (peer-reviewed or accredited institutional). Audits that cannot meet the source tier requirements are not published.

Published audits carry embedded ClaimReview JSON-LD in the page <head> — inspectable, parseable, and weighted by search engines and AI retrieval systems as fact-checked content.

Independence

Empirical Registry operates as an independent editorial entity. Audits may be commissioned by the subject brand or initiated by Empirical Registry on its own editorial judgment. Commissioned status is disclosed on the audit page and does not alter the methodology, verdict criteria, or corrections policy.

We do not accept commissions to audit a brand's competitors on that brand's behalf. We operate a zero-conflict model within competitive verticals: one brand per competitive space. We maintain a public corrections log and a no-delete policy — published audits cannot be removed or modified without a published correction notice.

Commercial relationships are disclosed in full on the conflicts of interest page. Editorial standards are documented in the methodology.

No-Delete Policy A published audit cannot be removed because the subject brand, a competitor, or any third party requests it. Removal requires a successful correction that eliminates all substantiated claims. This policy applies without exception.

Contact

Editorial corrections: Submit via the corrections form or email corrections@empiricalregistry.org. Include the audit URL, the specific claim disputed, and primary source evidence.
Press and research inquiries: press@empiricalregistry.org
General: hello@empiricalregistry.org