Every capability below runs on every shipment, on every tracking scan - and every prediction is graded against the real outcome when the package delivers.
A calibrated arrival window on every shipment, rescored on every scan. Honest by construction: when we quote 80% confidence, packages land inside the window 81% of the time - measured, not promised.
Average error of about two hours - 3.4× more accurate than the lane-average method most TMS tools use (6.5 hours).
Flags shipments that will miss their promised delivery date while they're still mid-transit - 3.7× better than rule-of-thumb methods. Time enough to reroute, upgrade, or tell the customer before they ask.
In live validation, an overnight into rural California, the kind lane averages miss badly, was called correctly with 9 hours of lead time.
Predicts whether a package will go dark - no scans - over the next 6, 12, or 24 hours, with 90–97% discrimination accuracy. Stalled freight surfaces hours before the carrier admits anything.
Open the carrier case while there's still time to recover the shipment - not three days after it vanished.
Missed sort waves, lane anomalies, and misroutes surfaced as they form - because the models know the carrier's schedule better than the carrier's own tracking page does.
Watched against 64,000 mapped facilities, 90,000 lane timings, and 6,300 scheduled sort and departure waves.
Which carrier wins each lane - with measured, counterfactual savings, not vendor claims.
Landed shipping cost before the label is bought, from your own lane history.
Fulfillment bottlenecks and dock congestion predicted before queues form.
Staffing needs from predicted order and shipment volume, days ahead.
The intelligence layer for fulfillment operations. Part of Orderly.
Every capability below runs on every shipment, on every tracking scan - and every prediction is graded against the real outcome when the package delivers.
A calibrated arrival window on every shipment, rescored on every scan. Honest by construction: when we quote 80% confidence, packages land inside the window 81% of the time - measured, not promised.
Average error of about two hours - 3.4× more accurate than the lane-average method most TMS tools use (6.5 hours).
Flags shipments that will miss their promised delivery date while they're still mid-transit - 3.7× better than rule-of-thumb methods. Time enough to reroute, upgrade, or tell the customer before they ask.
In live validation, an overnight into rural California, the kind lane averages miss badly, was called correctly with 9 hours of lead time.
Predicts whether a package will go dark - no scans - over the next 6, 12, or 24 hours, with 90–97% discrimination accuracy. Stalled freight surfaces hours before the carrier admits anything.
Open the carrier case while there's still time to recover the shipment - not three days after it vanished.
Missed sort waves, lane anomalies, and misroutes surfaced as they form - because the models know the carrier's schedule better than the carrier's own tracking page does.
Watched against 64,000 mapped facilities, 90,000 lane timings, and 6,300 scheduled sort and departure waves.
Which carrier wins each lane - with measured, counterfactual savings, not vendor claims.
Landed shipping cost before the label is bought, from your own lane history.
Fulfillment bottlenecks and dock congestion predicted before queues form.
Staffing needs from predicted order and shipment volume, days ahead.
The intelligence layer for fulfillment operations. Part of Orderly.