01 · Solve

Route optimization that compounds across verticals.

VRPTW solver. Hundreds of orders, dozens of constraints, routes in seconds. The constraint library is the moat, not the solver.

R-0042 · OWNED

Optimization solvers are commodities. The differentiator is the constraint-modeling layer built up across nine verticals over a decade. Every constraint a real operation needs is in the library. Mix and match per project, per request.

01

Dozens of constraints. Named and testable.

Not a generic VRPTW engine with a handful of levers. A constraint library the size of nine industries. Every one carries through to the plan or the plan doesn't ship.

  • Multiple time windows per stop
  • Load capacity across weight, volume, item count, passenger count
  • Territory optimization via polygons
  • Multi-depot with automatic depot selection
  • Mixed pickup and dropoff within one route
  • Vehicle dimension-aware routing (height, width, length)
  • Centrums — walking-distance pickup points
  • Mixed driving and walking segments
  • Dynamic time buffers per area or trip duration
  • Conditional next-stop logic for VIP rules
  • Fixed-line optimization for preset corridors
  • Soft constraints, tunable tolerance per run
02

Operator-chosen objective. Not one-true-metric.

Minimize vehicles. Minimize distance. Minimize duration. Balance across drivers. Per project, per run. The same operation flips objective when the season flips.

  • Real-time and historical traffic blended into cost
  • Balanced route distribution across drivers
  • Same-location service time optimization
  • Minimum vehicle working duration
03

Per-branch rules without a release cycle.

New customer with a unique rule set onboards in minutes. Per-branch, per-project configuration travels with each API request. The ops team changes routing policy without code changes.

Verticals

Where the engine ships.

14 crew rides around a flight slot. 400 grocery orders across a metro. One optimization core for both.

Observed

What the numbers look like in production.

2,400 → 1,020 min
Total drive time, 30-vehicle fleet
157 → 131 km
Distance reduction, 16-vehicle fleet
30-300 sec
Solve window, tunable per run
500 + orders
Per optimization run

Send us your hardest constraint scenario.

A real set of orders beats any demo. Thirty-minute call, honest deltas against your current plan.