374,000 test predictions, measured on the most recent three weeks of data the models had never seen. Every claim on this page comes from that held-out test - or from live shipments in transit.
Most tools give you a point estimate and hope. We sell calibrated probabilities: when we say a package will arrive within a window 8 times out of 10, it does - measured at 81% against an 80% promise.
We predict delivery windows and late risk - we don't guarantee delivery times. That honesty is the differentiator.
Models are evaluated on the most recent three weeks of shipments they never trained on - the same test a customer's live traffic poses. No cherry-picked lanes, no retrofit benchmarks.
We compare against the methods teams actually use - lane averages for ETAs, rules of thumb for late flags - not strawmen. The 3.4× and 3.7× multiples are against those.
Every prediction is stored and automatically graded against the real outcome when the package delivers. Accuracy claims come from this feedback loop, not a one-time study.
Beyond the held-out test, we ran predictions on live shipments in transit. Every on-time/late call was correct, and the delivery landed inside the predicted window in nearly every case.
An overnight shipment into a rural California address - the kind lane averages miss badly - called correctly with 9 hours of lead time. Window contained the delivery; on-time call correct.
First enterprise deployment in progress. Until we have their numbers, we show you ours - ask for the live validation data in a demo.
Built and validated on 7 million real shipments and 55 million tracking events across 11 carriers. From tracking data alone - no carrier cooperation required - the models reconstructed:
The intelligence layer for fulfillment operations. Part of Orderly.
374,000 test predictions, measured on the most recent three weeks of data the models had never seen. Every claim on this page comes from that held-out test - or from live shipments in transit.
Most tools give you a point estimate and hope. We sell calibrated probabilities: when we say a package will arrive within a window 8 times out of 10, it does - measured at 81% against an 80% promise.
We predict delivery windows and late risk - we don't guarantee delivery times. That honesty is the differentiator.
Models are evaluated on the most recent three weeks of shipments they never trained on - the same test a customer's live traffic poses. No cherry-picked lanes, no retrofit benchmarks.
We compare against the methods teams actually use - lane averages for ETAs, rules of thumb for late flags - not strawmen. The 3.4× and 3.7× multiples are against those.
Every prediction is stored and automatically graded against the real outcome when the package delivers. Accuracy claims come from this feedback loop, not a one-time study.
Beyond the held-out test, we ran predictions on live shipments in transit. Every on-time/late call was correct, and the delivery landed inside the predicted window in nearly every case.
An overnight shipment into a rural California address - the kind lane averages miss badly - called correctly with 9 hours of lead time. Window contained the delivery; on-time call correct.
First enterprise deployment in progress. Until we have their numbers, we show you ours - ask for the live validation data in a demo.
Built and validated on 7 million real shipments and 55 million tracking events across 11 carriers. From tracking data alone - no carrier cooperation required - the models reconstructed:
The intelligence layer for fulfillment operations. Part of Orderly.