AICE Power

Competitive Intelligence & Product Roadmap

Clip-on sensors find commercial building energy waste in real time.

Company Overview

AICE Power is a hardware-software startup that uses clip-on panel sensors to identify building energy waste. Serving multi-site operators across supermarkets, retail, fast food, hospitality, logistics, offices, and public buildings.

Latest Intel

Zeitgeist tracks private signals to determine where the company is heading strategically.

New Intel: AICE Power is using clip-on panel sensors to bypass BMS-heavy deployments. If the alerts prove reliable, supermarkets and multi-site operators get an energy savings product that pressures Schneider, ABB, Siemens, and integrators.

What They're Building

The company's public product roadmap & what they're committed to building.

Clip-On Panel Sensors

AICE centers the product on sensors that clip onto electrical panels and monitor circuits every minute without BMS integration.

Real-Time Waste Alerts

The software flags abnormal loads, equipment left on after hours, refrigeration drift, HVAC waste, and load imbalance before the bill arrives.

Supermarket Energy Monitoring

Recent company content points to food retail as an early wedge, especially refrigeration, lighting, HVAC, and demand response in supermarkets.

Energy Flexibility

The blog links granular monitoring to demand response, battery storage, spot contracts, and using refrigeration as flexible energy capacity.

Compliance-Led Energy Reporting

AICE publishes around BACS, CSRD Scope 2, ISO 50001, Decret Tertiaire, EPBD, CRREM, and GRESB as buyer-facing reporting hooks.

Founder and Key Execs

Rémi Bouteiller

Co-Founder and CTO (ENS Paris-Saclay, embedded systems, signal processing, AI-powered sensors, prior hardware work at Pulse Audition and Amira Health)

Grégoire Chomette

Co-Founder and CEO (MIT AeroAstro MSc, former NASA planetary-defense engineer)

Founder Force Multiplier

Bouteiller brings embedded systems, signal processing, and sensor-building depth, while Chomette brings NASA engineering and MIT aerospace training. That pairing fits a product where hardware deployment and analytical accuracy both decide adoption.

Funding History

2026 | Founded
2026 | Joined Pioneers batch in February
2026 | Joined Y Combinator Spring 2026

Competitors

Schneider Electric:

Schneider sells building and energy management systems that are broader and heavier than AICE’s fast sensor-led monitoring wedge.

ABB:

ABB competes through established electrification and building infrastructure, while AICE argues for faster deployment and equipment-level visibility.

Siemens:

Siemens building automation addresses large building systems, while AICE starts with lightweight panel sensors and operational waste alerts.

Traditional BMS integrators:

Integrators deliver custom building projects, while AICE’s pitch is lower installation friction without requiring a full BMS rollout.

AICE Power

's Moat:

The candidate moat is proprietary data: each monitored building can add circuit-level load traces that improve vertical benchmarks and waste detection, though proof is still thin.

How They're Leveraging AI

Equipment-Level Energy Disaggregation

AICE uses clip-on electrical-panel sensors to infer which equipment is driving building energy waste, aimed at supermarkets and other multi-site operators. The useful move is translating circuit traces into equipment-level blame, not another pretty energy chart.

Real-Time Energy Waste Detection

AICE flags abnormal building loads such as refrigeration drift, equipment left on after hours, HVAC waste, and load imbalance before the waste appears on the bill. It is selling operational intervention, not passive reporting.

Vertical Energy Benchmarks

AICE could build a proprietary benchmark layer as more buildings are instrumented, especially across supermarkets where refrigeration, lighting, and HVAC patterns repeat. This is the plausible data moat, if deployments scale.

AI Use Overview:

AICE’s edge is equipment-level energy disaggregation from clip-on circuit traces, likely using time-series signal processing and learned baselines to turn panel data into operational alerts.

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