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We’re delighted to share that RushOwl have raised $10M USD in our Series A round , read the news here.

RushOwl
Product · ChronOS

ChronOS - the intelligence layer for transit networks.

The AI engine that predicts demand, decides what to run next, and gets sharper with every route. Trained on live transit operations - not simulation.

A bus depot at dusk with glowing light trails tracing route connections converging into the interchange
How it works

Predicts. Decides. Improves.

  1. 01 · Predicts

    Passenger demand before it materialises.

    ChronOS reads live demand signals across routes, times and stops. It anticipates where capacity will be short before riders feel the squeeze.

  2. 02 · Decides

    Dynamic route and capacity, in real time.

    Decisions flow through the operating platform: which vehicle takes which route, how many seats go where, which shifts match which demand window.

  3. 03 · Improves

    Every route compounds the signal.

    Every completed trip updates the model. Accuracy on one route lifts the floor on the next. The system we deploy in year two is not the system we deploy in year one.

How it compounds

More operations in, more intelligence out.

Every trip we operate feeds the next one. The more routes, geographies and live conditions ChronOS observes, the better it forecasts demand, plans capacity, and lays the groundwork for what comes next in transit - including autonomous operations.

  1. Live operations as the data source.

    Trips, stops, vehicles and ridership flow directly from the networks we run across four markets. No synthetic datasets, no scraped signals - ground-truth operational data observed under real conditions, with real passengers, real drivers and real weather.

  2. The model compounds with each deployment.

    Every new route or geography widens the distribution the model sees. What ChronOS learns in one market informs what we try in the next - demand behaviour, driver decisions, service-level exceptions. The data asset grows faster than the headcount.

  3. Groundwork for autonomous transit.

    The operational record that sharpens today’s human-driven fleets is also the behavioural foundation for tomorrow’s autonomous buses and shuttles. Autonomy in transit is a data problem before it is a vehicle problem. We are accumulating the ground truth now, route by route.

Next step

Talk to the team behind ChronOS.

Ask us anything - from how it is trained to what it does when a route breaks. We do not demo what we have not deployed.

Talk to our AI team →
Talk to our team →