Buried in your setup step is the riskiest moment of the whole workflow. Claude unpivoting the downtime table is described as "a data modeling decision that would normally require human judgment", and that is exactly why it deserves a validation step the article does not show: if the AI misreads the grain of a wide table, every downstream aggregate is wrong while remaining perfectly plausible, and the conversational layer will happily narrate the wrong numbers with confidence. The queries you spot-checked came after that transform, so they inherit whatever it did. Did you reconcile total downtime minutes from the dashboard, the 1,388 figure, against a hand computation from the raw Excel before trusting the semantic layer? For a practitioner blog whose promise is honest benchmarks, a load-step reconciliation table would be a genuinely useful addition to the next test.