EnGen
EnGen develops energy recovery systems that capture waste heat from AI data centres and convert it into useful energy. The company's technology is designed to integrate with existing data centre infrastructure, closing the loop between AI compute, heat rejection, and power recovery. As AI workloads drive data centre electricity demand toward nearly double by 2030, EnGen addresses the structural gap where most consumed electricity becomes rejected heat.
- Clean Technology
- Energy
- Hardware
Funding
Founders
Product
Problem
AI data centres consume approximately 1.5% of global electricity, with demand projected to nearly double by 2030 as AI workloads scale. Nearly all of this electricity is ultimately converted into heat, yet most of that heat is currently rejected rather than reused, representing a significant recoverable energy loss.
Solution
EnGen is developing energy recovery systems that capture waste heat from AI data centres and convert it into useful energy. The technology is engineered to work alongside existing data centre infrastructure, enabling facilities to reuse what they currently waste. By closing the energy loop between AI compute, heat rejection, and power recovery, EnGen helps data centres reduce their environmental footprint while improving overall energy efficiency. The systems are designed for integration with current infrastructure, avoiding the need for complete facility overhauls.
Target Audience
Primary customers are AI data centre operators and hyperscale cloud providers seeking to improve energy efficiency and reduce waste heat, as well as colocation facilities supporting AI workloads.
Features
- Energy recovery systems that capture and convert waste heat from AI data centre operations
- Designed to integrate with existing data centre infrastructure without requiring major retrofits
- Technology that closes the loop between AI compute, heat rejection, and power recovery
- Solutions tailored for the continuous heat streams generated by AI compute racks
- Systems that address the structural gap in current AI data centre energy management