Turbine-level weather
Our physics-AI forecasting backbone resolves local wind at individual turbines. We have evaluated it across three ERCOT regions against HRRR.
The platform
One site.
A connected power view.
Weather, nodal prices, GPU load and on-site assets. Our goal is to bring them together in a forecast built around your site’s decisions.
From physics to economics
A foundation built.
A decision layer taking shape.
Site-level power exposure
Planned layers connect weather forecasts with nodal prices, GPU load and the on-site portfolio to understand when power is abundant or constrained.
Decisions in dollars
Planned decision support for flexible load, battery charge and discharge, and GPU scheduling. Site-level economic backtesting comes before operational pilots.
Validation & rollout
Start with your site.
Prove the economics.
Our early result is a 30% reduction in wind-forecast error versus HRRR across three ERCOT regions, using ERCOT SCED actuals. Financial performance is the next question to test.
A site-level backtest
Define the site, available historical data and the operational decisions that matter. Evaluate potential value in $/MWh.
A paid pilot
Test the forecast in an operating context with a partner team, using agreed performance measures.
A portfolio relationship
Our longer-term plan is to expand across sites with contracts priced per operating MW.
Build with us
Put your next megawatt
in a better hour.
Discuss a pilotWe’re working with AI infrastructure teams in ERCOT. Let’s explore a backtest for your site.