Sampleproof
A measurement evidence layer that versions query frames, provider conditions, methods, uncertainty and public claim language.
Vendors measure whether brands appear in AI-generated answers, but results vary by query, provider, time, locale and personalization. The supplied research confirms several answer and search interfaces and a July 2026 federal policy statement related to AI accuracy, but no reviewed substantiation infrastructure for these marketing claims.
Sampleproof freezes a predeclared query population and sampling rule before measurement. It records provider, method, environment, schedule, exclusions, missingness and confidence intervals, then links an exact public claim to the evidence and limitations reviewed by analysts and legal owners.
Sampling frame, query execution, provider output, coding decision, estimate, uncertainty, analyst finding, marketing claim, publication and regulator outcome remain separate. A signed receipt proves reproducibility history, not representativeness, causality or compliance.
The first release should reproduce one descriptive claim across one provider and one fixed frame. It excludes universal visibility scores, competitor rankings, causal optimization claims and regulatory certification.
Measurement, product-marketing or legal leader at an AI-answer optimization vendor publishing quantitative placement claims
A July 2026 policy statement and rapid vendor claims create current attention.
Standardized evidence can improve claims, while frozen methods can still institutionalize biased query populations.
The record contains four cross-references and one inbound link.
Measurement interfaces are live, claim substantiation is timely and frozen frames create a concrete durable artifact.
The buyer is broad, provider outputs are volatile, legal exposure evidence is indirect and no structural incumbent barrier is evidenced.
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