Optimization at enterprise scale
Our first pilots did what we hoped: the engine held up on real, focused problems. v1.2 rebuilds what sits beneath it to work at the size a large operation actually runs.
Dedicated optimization backend
Heavy solving runs on its own backend, driving MILP solvers like SCIP.
Hundreds of thousands of variables
Models with hundreds of thousands of variables and constraints now solve as ordinary jobs.
Queued, distributed solving
Solves are queued and spread across the backend, so one long job never freezes the platform for everyone else.
Integrated simulation
A discrete-event simulation engine runs each optimized plan forward to see how it holds up under real operating conditions.
Multi-model pipelines
Chain models into a pipeline: optimize one stage, then feed its output into the next.
Byte-for-byte reproducible
The same inputs always produce the same model, and each one carries the decision trail that built it.
The early pilots were deliberately small: narrow, well-defined problems, to find out whether the core idea held. It did. v1.2 keeps that result and makes the problems much bigger.
From pilots to production scale
The pilots backed the idea LogiModel is built on: compile a formulation deterministically and you get decisions you can audit line by line. Every model is byte-for-byte reproducible (same inputs, same model) and carries the decision trail that built it.
The real change is what happens once a model is built. Heavy solving runs on its own backend, driving MILP solvers like SCIP. Models with hundreds of thousands of variables used to be past what we'd attempt. Now they're ordinary jobs. Solves are queued, so one long job never freezes the platform for everyone else.
Built for the complexity of real operations
Real problems don't stay still once solved. A fleet routed perfectly on paper still comes apart when a depot runs late or demand shifts mid-week. v1.2 wires the optimizer to a discrete-event simulation engine, so every plan runs forward against those conditions before anyone commits.
Chain models into pipelines: one stage's output feeds the next. A plan that solves cleanly and survives the simulation is one you can defend in the field.
Ready for clients at enterprise scale
It comes down to the client we can now take on: a national network with dense routes, heavy volume, and service-level targets it can't miss. v1.2 was built for those conditions, on an engine proven in real pilots first.
The pilots are finished and v1.2 is live. If you're operating at that scale, we'd like to put it in front of your problems.
Run it at the size you actually operate.
The platform is live. Bring a real scenario and we'll run it through the optimizer and the simulation at your scale.