TruthPath
A citation-measurement workspace that freezes query frames, sizes repeated samples, reports uncertainty and multiple-testing limits, and keeps visibility observations separate from pipeline attribution and causal claims.
Brands increasingly track whether AI search experiences cite or mention them, but repeated answers vary by engine, model, location, session, query wording, and time. Research confirms a well-funded incumbent processing millions of daily citations and several trackers, while the supplied practitioner signal argues that existing tools monitor noise rather than establish reliable change. TruthPath adds preregistered query frames, minimum sample planning, controls, uncertainty, missingness, and source-linked downstream observations. Statistical significance is not practical importance, a citation is not a visit, attribution is not causality, and revenue remains an observed business outcome outside the measurement claim.
The growth, content, brand, search, or marketing analytics leader at a mid-market B2B company investing in AI-search visibility and needing defensible reporting.
A May 2026 six-week practitioner experiment and well-funded incumbent establish a current buying window.
Sampling, normalization, analysis, evidence, alerts, and reporting are code-scalable.
One cross-reference and no inbound connections provide limited convergence.
The category and incumbent scale are confirmed, practitioner criticism supports a rigor wedge, and repeated sampling plus reporting are software-scalable.
The buyer and budget remain underspecified, only one original-stage interface was verified, provider behavior changes, revenue linkage is incomplete, and incumbents can add statistical features.
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