Turning a 50%-false alarm into one investigators trusted
Fuel loss compounded quietly across the fleet, and investigators were drowning in bad alerts. I built the signal pipeline and detection that made the alerts worth opening again.
- Role
- Built the signal pipeline and detection
- Scale
- ~2,500–3,000 trucks, readings every ~1–2 minutes from three providers
The mess
Half the alerts were false, so investigators stopped trusting all of them, and real loss hid in the noise.
The question
Is this fuel event worth an investigator’s time?
The call
Clean the signal before flagging anything. Three providers disagreed with each other and with fueling records, and no model downstream could fix that.
What changed
- False alerts ~50% → ~15%
- Alert precision ~0.90
“The model was never the hard part. Making the signals agree was.”
Delivered within a prior employer’s environment. Details are intentionally anonymized. I’m glad to discuss the operating problem, design principles, and production tradeoffs.
Have a workflow that looks like this?
Let’s work out whether AI is actually the right lever.