FleetMind

The situation

Professional fleets are electrifying while the electricity system is looking for consumption it can steer. Those two curves meet in the depot, at night, in a plan somebody has to commit to before the night happens.

The problem

The operational bet is made once, in the evening, against a night that has not occurred yet — and the tools available are averages, spreadsheets and experience. The decision is not “what will the load be”. It is “which response do I take now, and does it still hold if the night goes differently”.

What I built

Carry forward exactly two things from the prototypes and build the product around them. First, uncertainty is explored rather than averaged: the operational night is sampled many times and interventions are compared across the same sampled futures. Second, the language boundary is structural: the simulation owns the arithmetic, the model explains it, and nothing reaches the operator that has not resolved to a computed value.

FleetMind is the active product direction that DepotOps and Forkcast were, in retrospect, both arguments for. DepotOps established that operational risk becomes legible when you simulate the night hundreds of times and phrase the result as a count of concrete outcomes. Forkcast established the boundary that makes such a system safe to put in front of an operator: the engine owns every number, the model owns only the explanation, and the boundary is enforced by verification rather than by instruction. FleetMind takes both and points them at electric fleet and charging operations as a product rather than a demonstration.