SprintiQ™Backhaul Agent
A predictive dispatch system for drayage trucking. It forecasts container availability so trucks route toward loads instead of returning empty, turning a deadhead trip into a loaded one.
A quarter to two-fifths of every drayage mile is empty.
Drayage is the short-haul movement of containers between ports, rail yards, warehouses, and distribution centers. Industry estimates put the deadhead rate, the share of miles run empty, at 25 to 40 percent. A truck drops a container sixty miles inland and returns to port with an empty chassis, burning fuel, hours, and margin for zero revenue.
The reason is structural. Dispatch is reactive: a truck finishes a delivery and waits for the next load to appear. If a dispatcher knew, with enough confidence, that a container was available for pickup nearby, that empty return becomes a loaded one. Roughly double the revenue per trip, roughly half the cost per move.
Turn dispatch from reactive to predictive.
Today dispatch is reactive: a truck finishes a delivery and waits for the next load to appear. Backhaul Agent makes it predictive. It forecasts, with calibrated confidence, where and when a container will be available for pickup, so a truck routes toward the next load instead of deadheading back empty.
The forecast draws on operational, supply-side, and demand-side signals to reconstruct the container journey ahead of time. A dispatcher asks, in plain language, where the nearest pickup is for a truck that just delivered, and gets ranked recommendations with confidence scores, weighing availability against proximity and the cost of running empty. The modeling approach is proprietary; the outcome is a loaded return where there used to be an empty one.
Prototype in under two weeks.
MVP in build.
SprintiQ Turbo took Backhaul Agent from kickoff to a working prototype in under two weeks. We tested it. The MVP is being built on what it taught us.
SprintiQ Turbo built the prototype end to end, data pipeline to prediction layer to dispatch agent, in under two weeks. It tested two questions: does the data carry enough signal to forecast availability, and will a dispatcher act on the recommendations? Both held.
The MVP is now being built on what the prototype proved out.
Other projects.
Each one built on SprintiQ, each one a different proof of what the practice can do.
Have a prediction
problem?
If your operation leaves money on the table because dispatch is reactive, a predictive layer is exactly the kind of engagement SprintiQ takes on. Email the principal directly.