Physics.
Data.
AI.

oorja builds physics-guided AI software for battery engineering: BattForge simulates cell and pack behaviour before you build, and BattPrism monitors deployed batteries in the field to predict battery risk before failure.

It’s machine learning constrained by the governing physics of the system (equations, boundary conditions, material behaviour) rather than learned purely from data. It makes predictions more reliable despite limited data availability.

A platform where physics-guided AI and machine learning work together: physics keeps predictions grounded in real battery behaviour, ML allows models to sharpen as more field and test data comes in, and AI provides meaningful insights.

To reduce cost and risk across the battery lifecycle: simulating cell and pack design, predicting degradation and Remaining Useful Life (RUL), and producing a verifiable health report during resale.

Our physics-based models are validated against real experimental and lab data, so results are built to track physical testing rather than just approximate it. For accuracy figures on a specific chemistry or use case, our team can walk you through the relevant validation data.

No, we enable automotive OEMs, EV fleets, used-EV and resale platforms, and Battery Energy Storage System (BESS) operators, since each carries battery risk at a different point in the lifecycle.

Every subscription includes onboarding and access to our engineering team to get the platform running against your own data. For what’s included at each tier, reach out to us and we’ll walk you through it.