We spent 11 weeks building a credit scoring model that was confidently wrong.

Not because the architecture failed — because 40% of the training labels were garbage nobody had touched since 2019.

Performing loans tagged as defaults.

Defaults hiding under "restructured."

Clean dashboards, zero error messages, and money quietly bleeding out for months.

In this episode:
— What actually went wrong and how long it took anyone to notice
— The mango-labeled-"apple" problem with ML training
— Why bad data doesn't crash your system — it just lies to you politely
— The 3-week data rebuild that took accuracy from 61% to 89% without changing a single line of model code
— Why 60% of your AI budget should go to data ops before you touch the model
— The 500-row test every team should run before writing model code
— Why the absence of complaints is not evidence of data quality

If you're building with ML this year — at a startup, at an enterprise, or at a company that just got budget approval for "an AI initiative" — this is the silent failure mode nobody puts in the pitch deck.

The fanciest architecture in the room is only as smart as the spreadsheet it trained on.