Exowatt builds modular, dispatchable solar systems that deliver reliable power for energy-intensive applications such as AI data centers. Its flagship product, the Exowatt P3, captures solar energy through optical lenses, stores it as heat in a long-duration thermal battery, and dispatches electricity on demand through a heat engine. Unlike grid-tied photovoltaic solar systems, P3 powers data centers directly behind the meter, helping data centers come online without waiting in multi-year grid interconnection queues. Exowatt builds a fleet of AI agents that monitor and triage live alarms and support operational decisions for solar systems, often at the edge, with limited or no connectivity. As its fleet scales from test benches toward manufacturing and thousands of deployed field units, the intelligence layer that operates these systems must scale with it.

In this post, we show how Exowatt used Amazon SageMaker AI serverless model customization to build a repeatable AI pipeline. The pipeline produces task-specific fine-tuned models for two deployment targets, edge and cloud, achieving higher accuracy than frontier models at up to 49× lower cost.

The two models described here are the first outputs of that pipeline rather than the end goal. The more durable result of the engagement was the pipeline itself: a repeatable path from raw operational data to a certified training set, a fine-tuned model, and an evaluated deployment artifact. Exowatt can now run this path in days rather than months. Alarm triage and energy-market analysis are two of many narrow, high-value tasks where the same approach applies.

Exowatt’s energy management system (EMS) runs across a growing fleet of operational sites.

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