One brand. One fleet. Tuned to your streets, not a generic city model.
Running your own last-mile is a specific problem - your volume shape is stable, your service area is fixed, your window promises are yours to keep. The engine tunes against that shape rather than averaging across a shared pool.
Corridor-level tuning to your own operation.
The engine learns the corridors you actually run, the hubs you actually pick from and the windows you actually promise. A last-mile fleet with a 4-hour window has a completely different reallocation cost profile than an on-demand food fleet - and it should be treated that way.
- Corridor history built exclusively from your operation, not a shared pool
- Reallocation cost calibrated to your handover overhead and rider mix
- Rider app in your brand, with your operational vocabulary
- Custom escalation flows into your existing ops tools
A last-mile shift
Manifest ingested
Day's manifest lands. Engine plans a first cut against 42 riders and 3 hubs.
First reassignment
Rider stall. 6 orders reassigned across 3 other riders. Both windows saved.
Batching wave
Post-lunch batching wave. 18 pairings created across the residential corridor.
Manifest-2
Second manifest arrives. Engine folds it into existing plans without disturbing on-window orders.
Wrap
On-time rate 96.1%. Window accuracy 93.4%. Reassignment reasons logged.
Six things you can stop doing manually.
Manifest planning
Automated planning against your fleet at shift start. Manual re-planning becomes exception-handling, not a job.
Window recovery
Windows about to slip get worked automatically - a closer rider is proposed and the move happens before the miss.
Rider load balancing
Fairness guardrails keep the fleet distribution honest across a shift - no rider quietly buried while another sits idle.
Customer status
Live tracking pages and event-driven notifications run themselves against your brand - no dispatcher paging updates.
Ops review
Weekly ops reviews come with the analytics already built - corridor drag, SLA drift, reassignment ROI ready to read.
Escalations
Genuine unresolvable exceptions raise into your existing ops tools, in the format your ops team already uses.
All riders shown are drawn from your fleet. No pool, no sharing - the tuning is entirely yours.
See the last-mile setup on your streets
We will take a live slice of a recent shift and walk through what the engine would have done - route by route, reassignment by reassignment.
Show us your fleet size. Watch the swarm move at your scale.
Type a rider count and a rough daily order volume. The view on the right rebuilds to that scale, with a sample reassignment playing every few seconds - the same kind of decision the delivery engine makes for a live fleet.
An illustrative view based on typical patterns - not a live read of your actual fleet. Numbers move with your inputs so you can see the shape of the problem the engine solves.