Actionable forecasts for a chaotic world.

Power grids, roads and buildings generate thousands of signals across separate systems. They are noisy, disconnected and difficult to read together, leaving operators with too little warning when conditions change.

Teams often react after a price spike, traffic surge or equipment failure. Forecasts can miss these extremes because their causes sit beyond any one data stream. A heatwave can raise building load, grid demand and energy prices at once.

These systems share drivers: weather, people, events and daily rhythms. Reading their signals together gives operators a better view of what may happen next.

DataCruiser learns how physical systems move across space and time. It combines operators’ signals to find shared patterns, sudden changes and links between places.

Forecast is available today. Simulate lets operators explore what-if scenarios and is in development. Act is in development, with a goal of recommending the next best move for operators.

Early results span energy markets and road traffic, two domains with different signals and operating rhythms. Pilots on operators’ own data are where we validate the model for each system.

The same model can adapt to a new domain with its data and a light adapter, without building a separate model for every task.

Energy markets and grids.

Energy prices, demand, generation, outages and weather all shape the decisions operators make.

Road traffic.

Sensor counts, incidents, roadworks, events and weather help reveal what is changing across a network.

Buildings and assets.

Telemetry, occupancy and equipment signals can help operators anticipate building and asset needs.

Air quality.

Weather, traffic and monitoring-station data help build a clearer picture of local air quality.

Movement of people and vehicles.

Vehicle trips, routes and timing help forecast where people and fleets will move.

Airports, ports and logistics and fleets are other domains where we see this approach applying.