@alexcloudstar Thanks Alex! Great question. Alerts & Automation is exactly why we built the system the way we did.
We tackle false positives through a two-layer architecture. Our analyst agents (fuel, maintenance, safety, etc.) don’t just flag anomalies. They calculate actual dollar impact and filter out anything below a meaningful threshold. So instead of “battery voltage dropped,” you get “Unit 2847’s battery will fail in 12-18 days, $380 tow + $1,200 downtime.” On top of that we have years of data to compare against to make sure an anomaly looks like something we’ve seen in the past (or close to)
But the real breakthrough is our persona agents. They interpret the technical findings through the lens of someone who’s actually run fleets. Understanding things like “this driver’s harsh braking looks bad in the data, but he’s hauling through the Rockies where it’s normal” or “this maintenance delay is actually smart because the part is backordered anyway.”
Every insight comes with specific vehicle IDs, dollar amounts, and the political context a real fleet manager would consider. We’d rather surface 5 high-confidence issues worth $10K than 50 maybes worth $100.
The team’s been amazing on this. Especially the folks who’ve lived in fleet operations and know what actually keeps managers up at night versus what’s just noise in the data.