Our Team
Energy Quotient builds products at the intersection of power systems, modern software, and edge intelligence, with deep expertise spanning electrical distribution through embedded AI.
Kevin Davies, PhD
Founder & CEO
Kevin brings 25 years of experience in electrical, mechanical, and software engineering. As former faculty at the University of Hawaii and an engineer at Ford Motor Company, he developed deep expertise in power systems, battery technologies, and renewable integration.
He founded Energy Quotient (EQ) to build the tools he wished existed — rooted in a long-standing commitment to smarter, cleaner, and more resilient energy systems.
Kevin is a co-inventor on multiple power system patents, including two awarded and two pending. He earned his PhD in Mechanical Engineering at Georgia Tech, where his research focused on multiphysics modeling of fuel cell systems.
Anthony Reffle
Technical Co-Founder & Software Lead
Anthony co-leads technical development at EQ, focusing on embedded systems, processing, data infrastructure, and edge AI. He has helped extend and refine the company’s real-time waveform streaming pipeline and has built a modular, memory-safe Rust-based platform for power quality analytics.
His expertise spans Rust, Linux kernel development, and real-time systems engineering — enabling the low-latency, deterministic performance required for mission-critical power applications. Most recently, Anthony architected EQ’s edge-deployable AI stack, including a vectorized retrieval engine and SLM routing layer for real-time, signal-grounded intelligence.
Anthony brings a rare combination of systems thinking, hands-on engineering depth, and creative autonomy — the backbone of EQ’s real-time software stack.
Why This Team
Our competitive advantage comes from a combination of capabilities rarely found together:
- Hardware-software integration: We control the full stack — from custom sensor hardware to edge-deployable AI
- Real-time systems expertise: Kernel-level programming meets power systems engineering
- Practical AI application: Machine learning grounded in physical understanding and operational context
This combination allows us to do what neither traditional hardware vendors nor software-only companies can: deliver laboratory-grade monitoring at industrial scale, with intelligence built for action — not just observation.