GridWise AI uses live grid carbon data and optimization to schedule flexible workloads into their lowest-emission window — without changing what gets computed or missing a deadline.
Grid electricity is not equally clean every hour. Sometimes a lot of power comes from wind, solar, or nuclear; other times gas or coal plants carry more of the load. The same electricity use can mean more or less CO2 depending on when you use it.
Many compute jobs don't need to start this second — they only need to finish by a deadline. Yet teams still run them immediately, often landing in high-carbon hours. Same work, worse timing, avoidable emissions.
GridWise pulls real hourly carbon intensity from Electricity Maps, computes the lowest-emission contiguous window that still meets your deadline, and shows exactly how many kg CO2 you save versus "run now".
A Gemma-powered AI agent then explains the decision in plain language so you can see exactly why one window beats another.
Connect data →Among all valid start times before your deadline, GridWise picks the window with the lowest total emissions using real grid signal, not estimates.
A Gemma-powered agent narrates the schedule in plain English — why that window, which hours were dirtiest, and how much CO2 was avoided versus baseline.
Optional ElevenLabs voice rendering reads the schedule rationale aloud — useful for screen-free monitoring or accessibility.
No new hardware. Just a smarter scheduler that aligns flexible demand with cleaner generation — including firm low-carbon power like nuclear baseload.
Connect data →In 2025, global electricity demand grew 3%, with data centers among the fastest-growing contributors — accounting for around half of total U.S. electricity demand growth. At the same time, grid emissions are not constant: hourly carbon intensity can change significantly depending on generation mix, imports, and system conditions. That creates a clear opportunity: same compute, smarter timing. Instead of changing the job itself, GridWise changes when it runs so flexible workloads align with lower-carbon hours while still meeting deadlines. This approach also helps align demand with firm low-carbon supply like nuclear baseload, reducing reliance on fossil peaker plants.