Industries
One architecture, many assets.
Native-resolution ingestion, a physics-calibrated twin of the specific asset, and models that learn on top of that twin. The substrate doesn't change between sectors — the physics and the failure modes do.
Sectors
Solar
Utility-scale generation, loss attribution, and fleet normalisation.
DeployedRenewables
Wind, hybrid, and storage assets under one operating picture.
In discussionUtilities & Grid
Transmission and distribution assets, load behaviour, and network health.
In discussionManufacturing
Process lines and rotating equipment, modeled before they fail.
Architecture readyWater
Treatment, distribution, and pumping systems with sparse instrumentation.
Architecture readyInfrastructure
Distributed physical assets that report continuously and fail expensively.
Architecture ready
Why solar first
We started with the hardest one.
Solar couples everything that makes industrial optimization difficult: dense telemetry at five-minute resolution, physical degradation that compounds silently, weather coupling that moves the baseline hourly, heterogeneous hardware across brands and vintages, and a financial consequence attached to every hour of underperformance.
A system that separates soiling from mismatch from inverter drift on a real fleet has already solved the general problem: distinguishing expected behaviour from anomalous behaviour on a physical asset you cannot inspect directly. That is the same problem in a pump station, a transmission network, and a production line.
See what your operation is actually losing.
A walkthrough on your own asset class, with a scoped pilot on one to three sites if it's a fit.