Lexicon
AI Résumé
What an AI Résumé means, and how Paul Truscott applies a statistical measurement lens to track the accuracy and consistency of a brand's composite representation across AI systems.
Factual Definition
An AI Résumé, coined by Jason Barnard and Kalicube, is the composite representation of a brand or individual across AI-generated answers and assistants, functioning as the AI-era equivalent of a Brand SERP: what AI systems collectively say about an entity when asked.
Paul Truscott's Perspective on the AI Résumé
Paul Truscott's perspective on the AI Résumé is that it should be audited across systems for consistency, not just accuracy within any single one. His view is that an entity can be described accurately by ChatGPT, accurately but incompletely by Gemini, and inaccurately by Grok, and that this inconsistency across systems is itself a diagnostic signal, usually pointing to fragmented or thin corroboration that different AI systems have weighted differently. A brand's AI Résumé is not a single document; it is a distribution of answers, and the variance within that distribution tells Paul as much as any individual answer does.
He treats disagreement between AI systems about the same entity as a direct signal that corroboration depth is insufficient to produce a stable, consistent representation, in the same way Dow Theory treats disagreement between market indices as a sign that a trend is not yet confirmed.
How Paul Truscott Applies AI Résumé Auditing
Paul tests the same set of factual queries about a client entity across ChatGPT, Gemini, Grok, Perplexity, and Google's AI features, comparing the resulting answers for consistency as much as for individual accuracy. Significant variance between systems is treated as a corroboration gap requiring remediation, rather than accepting that "some systems get it right" is a sufficient outcome. Read the full methodology.
Why the AI Résumé Matters to Paul Truscott's Practice
The AI Résumé is the composite output every piece of Paul's corroboration and entity identity work is ultimately trying to shape, and his practice of measuring consistency across systems, not just correctness within one, reflects the cross-source confirmation discipline central to his approach. Read the full analytical foundation this discipline is built on.