Our AI Feature Passed the Spec—Then Real Data Exposed the Problem
We recently built a new Attention Tier system for FounderFlow to classify business emails as Act now, Review today, Monitor, Routine, or No action.
The first version followed the written specification—but a production-data dry run revealed that it placed 86.1% of all emails into one tier. Technically correct. Practically useless.
The problem was a signal that appeared reasonable in the specification but was also the default value for unclassified emails.
After recalibrating the system against 6,037 production emails, the distribution became:
• Act now: 3.2%
• Review today: 8.9%
• Monitor: 50.7%
• Routine: 33.4%
• No action: 3.8%
Testing uncovered another defect: 222 replied-to or archived emails were still demanding attention, including 27 incorrectly labeled “Act now.”
The lesson: a feature is not validated because it matches the specification. It is validated when it produces useful results with real data.
FounderFlow is an AI Executive Chief of Staff designed to help founders separate important business signals from everyday inbox noise, identify risks and revenue opportunities, and act before critical matters are missed.
Have you ever built something that passed every requirement but failed when tested against real behavior?
