SWITCH built an Agentic AI platform for urban fleets, combining simulation, forecasting, and real-time decision-making to automate planning, routing, and ops in mobility and logistics.
Under the hood
The engines that make Myles different.
Every answer Myles gives, every forecast, every rebalancing call, runs on two proprietary engines built only for urban fleets. Not a general model with a mobility wrapper. Built from the ground up for this one problem.
TECHNOLOGY
From data to executed decisions
Analytics
Every trip, vehicle and charging event lands in a single live model of your network. Dashboards and API-ready metrics turn fragmented operational data into one shared picture of what is actually happening on the ground, city by city.
Forecasting
Pre-trained e tailored demand models powered by your own historical patterns, weather, events and seasonality, reaching up to 98% demand-forecast accuracy. You see where trips will start tomorrow, not just where they started last week.
Operations
Rebalancing, charging and maintenance are computed continuously, not once a day. Agentic AI proposes the next action for every vehicle and every crew, then adapts as conditions on the street change.
Planning
Test a decision before you fund it. Fleet sizing, zone design and placement of parking, hubs and charging stations are simulated on a digital twin of the city, so capital goes where demand says it will pay back.
WHY IT MATTERS
Why you can’t get this from a general AI.
ChatGPT and every other general-purpose model were trained on text. They can describe what a fleet is. They cannot understand how your fleet behaves – because that knowledge doesn’t exist in text. It exists in billions of trip records, zone-level demand signals, and years of operational patterns across thousands of cities.
That’s what Pulse-AI and OptiMesh were trained on. That’s the data advantage that makes Myles answers different. Not better prompts. A different kind of intelligence.
