The Evidence Ledger: Making AI Claims Auditable

Every AI claim should be traceable. When you say an AI system delivers 17× ROI, regulators, investors, and skeptics deserve to know exactly how you measured that.

The Evidence Ledger is a documentation framework that makes every metric auditable by answering four questions for every claim: (1) What was measured? (2) What was the baseline? (3) What assumptions went into the calculation? (4) What are the limitations?

Take Nubank's foundation model: 2.56× ROI. How does that hold up to scrutiny? They measured it by comparing customer churn, transaction volume, and portfolio quality before and after deploying the model. The baseline was clear: historical performance on the same customer cohorts. The assumptions were documented. The limitations were explicit.

Compare that to an AI project that claims "massive efficiency gains" but never defines what that means. No baseline. No measurement methodology. No limitations. That's not an achievement—that's marketing.

This methodology-first approach builds the credibility that separates serious deployments from hype. You'll learn how to structure evidence so that stakeholders can validate claims independently.

About Infinidatum

Infinidatum is an AI deployment intelligence company with extensive experience in enterprise AI across financial services, healthcare, insurance, and regulated industries. Our tools and insights are backed by data from $500M+ in real-world deployments and 100+ documented case studies — $100B+ value delivered, 380% avg ROI.

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