Optimize Your Battery Systems with Predictive Intelligence

Predeeption helps operators, integrators, and energy providers manage large groups of batteries—whether in EVs, e-bikes, or energy storage systems—with clarity and confidence. Our platform turns raw battery data into clear insights, showing what’s happening now and what might happen next. From real-time checks to early warning alerts and long-term performance trends, Predeeption makes it easier to keep batteries healthy, efficient, and reliable throughout their entire life cycle.

What Predeeption Provides:

• Real-Time SOH, SOC & RUL Estimation: Continuous and precise evaluation of battery State of Health, State of Charge, and Remaining Useful Life using real-world signals, usage data, and environmental conditions.
• Mid- and Long-Term SOH Forecasting: Anticipate battery aging trajectories under varying operational scenarios to plan maintenance, replacement, and second-life applications with confidence.
• AI-Driven Anomaly Detection: Identify abnormal usage patterns, early signs of degradation, and potential failures with robust models trained on extensive multi-domain battery datasets.
• Fleet-Level Analytics and Insights: Analyze and compare battery behavior across entire fleets—whether EVs, e-bikes, or BESS—to detect hotspots, usage trends, and environmental impacts.
• Usage Profile Identification: Classify and interpret real-world usage scenarios—fast charging, urban driving, temperature extremes—to better understand and optimize operational conditions.

About the team:

Behind Predeeption is a team of engineers, scientists, and builders who care deeply about solving real problems in energy and mobility. We bring together expertise in battery systems, data science, software engineering, and product design to create tools that are reliable, simple to use, and genuinely helpful. Our team works closely with partners and operators in the field, making sure every feature we build reflects real-world needs and delivers practical value for anyone managing batteries at scale.