Predeeption

About Predeeption

In a world accelerating toward electrification, batteries are everywhere — powering vehicles, homes, grids, and industry. But while their role is growing, their management remains reactive, inefficient, and costly. That’s where we come in. Our deeptech startup (CNRS spin-off) develops AI-driven battery foresight to transform raw data into predictive intelligence. We combine advanced physics-informed models, and generative AI to anticipate battery behavior, detect early degradation, and extend lifetime. Our solution helps: Fleet operators reduce downtime and plan smart maintenance OEMs and integrators optimize design and usage strategies Energy providers secure storage and grid reliability Battery lessors assess value and enable second-life use Unlike conventional monitoring, our platform doesn’t just report — it predicts. We deliver actionable insights: remaining useful life, state-of-health evolution, risk forecasting, and usage recommendations. Born from leading research at CNRS and RS2E, our team blends electrochemistry, AI, and software engineering. We’ve built a scalable, API-first architecture for real-time or retrospective battery analytics across diverse chemistries and formats. Our goal? Enable batteries that think ahead — reliable, efficient, and sustainable. With AI-powered foresight, we’re building the intelligence layer the battery revolution needs. Let’s bring clarity to battery uncertainty, together.

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