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Statistical and machine-learning methods reconciling multiple model outputs, running in production long before "AI" was the industry's word for it.
A proprietary global atmospheric model built at the intersection of supercomputing and machine learning.
First implementation of the JEDI data-assimilation system, advancing how machine-driven forecasting improves at the model level.
A proprietary network of ground-truth weather stations feeding the models real-world data most AI-weather startups don't have access to.
Decades of archived weather data, now unified and queryable through modern AI tooling internally.
An AI assistant already live inside a consumer product, Storm Radar. Proof this isn't slideware.
GRAF's variable-resolution grid tightens where the atmosphere demands it, resolving weather at kilometer scale. Finer grids, continuous data assimilation, and ensemble forecasting, the same machine-learned modeling we ran in production long before "AI" was a pitch-deck word.
2.2 billion forecasts produced daily · trillions of API calls per month
Preflight to touchdown. Flight-safety and delay-avoidance intelligence for carriers and airports.
LEARN MORE →Operational weather intelligence for supply chains, energy, defense, and public-sector missions where the forecast has to be right, not just interesting. Validated with MITRE.
LEARN MORE →Enterprise-grade production tools and visualization data powering local and global broadcasters.
LEARN MORE →Continuous streamline modeling gives aviation and energy teams the lead time to move before conditions reach them.
Breeze Airways integrated weather tracking into flight operations, saving flights in the Florida market during Hurricane Milton and gaining enhanced visibility into current and future air-traffic conditions.
READ CASE STUDY →Improved workflow and dynamic on-air graphics let this Hattiesburg, Mississippi station respond swiftly to evolving severe weather when seconds counted.
READ CASE STUDY →A research collaboration to push global weather intelligence forward at the infrastructure level. Proof this isn't just a vendor relationship, it's a science partnership.
READ CASE STUDY →To learn more about applying decades of weather AI to the decisions only machines can make fast enough, talk to our team.
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