Solving climate change with massively scalable, impactful technology.


The Core Problem

The Pedersen Glacier in Alaska, 1917 vs now.

At its heart, climate change is an emissions problem. Since the Industrial Revolution, humans have added unprecedented amounts of emissions to the atmosphere that trap heat and warm the Earth.

Reducing emissions, or decarbonization, is typically achieved with electrification. Yet, electrification doesn’t truly reduce emissions unless the source of the electricity itself is clean.


Scale is Critical

A solar-plus-storage power plant.

Transforming the grid to 100% clean electricity is a planetary effort constrained by limited time and resources. Any problem we work on—and any solution we implement—is focused on having maximum impact at a massive scale, in the most resource-efficient way possible.

This is where machine learning plays such an important role: scaling impact with minimum resources.


Our Technology

During the clean energy transition, power plants and grid operators will be in a constant state of adaption. Any inefficiency in this phase leaves critical clean energy resources underutilized.

We specialize in developing novel, self-optimizing, self-adapting systems to control clean energy infrastructure to maximize its impact in a highly scalable way.


Backed by the Best

Founded in the fall of 2022 and backed by Y-Combinator 2023, we raised a $1.5M seed round to build the first version of our tech and bring it to market.

Since then, we’ve completed v1 of our models, achieved regulatory certification in CAISO (California’s grid), and started operating in a real electricity market.


Join Our Team

Our Venice, CA office.

With our energy trading pilot live, and a clear path to self-optimizing batteries, we’re starting to grow the team at Keeling Labs to build out and scale critical components of our tech stack.

We’re looking for mission-driven, experienced engineers (not necessarily from the energy space) to join our team and make a massive impact on the climate problem.