How AI can cut factory energy costs without new gear

This article is a part of Startup Spotlight, a series that features young, up-and-coming startups.

Image credit: Timmy Loen

During her career in enterprise AI and data systems in finance, Kathryn Knight saw a recurring pattern. The physical world of factories and infrastructure generated vast amounts of data, but most of it sat unused. 

The idea for Muun AI came from realizing that machines assumed to be identical actually develop unique performance profiles over time. Yet, control systems still treat them as a uniform fleet, hiding major inefficiencies in plain sight.

😟 Problem

Most factories already collect huge amounts of data from their equipment. However, industry estimates suggest that 50% to 80% of this operational data is never analyzed or acted upon. This forces plant managers to operate using static rules rather than real-time machine performance.

This gap leads to hidden waste, such as machines running longer than necessary or energy systems staying active after a process is complete. Operations teams are under pressure to cut costs and improve output, but often lack the tools to turn raw machine data into simple, actionable insights.

💡 Solution

Muun AI analyzes machine data that factories already collect to find operational inefficiencies. The platform connects to existing industrial systems like Supervisory Control and Data Acquisition (SCADA) and historian databases in a read-only mode.

It then uses machine learning to identify simple ways for operators to improve performance, including:

• Builds a performance baseline for each machine, not the entire fleet.
• Automatically detects when a machine is running longer or using more energy than needed.
• Surfaces clear, ranked insights that operators can act on without new equipment.

Image credit: Muun AI

📊 Market size

The global manufacturing industry is valued at US$16.8 trillion, with an estimated 7 million to 10 million factories in operation. Muun AI targets instrumented facilities, and capturing just 1% of this market could translate into US$500 million to US$1 billion in annual revenue.

A single proof-of-concept deployment identified around US$300,000 in annual energy savings, highlighting the significant value available even within a small group of machines at a typical facility.

🤝 Team

Kathryn Knight. Founder and CEO. She has experience scaling AI-driven fintech operations at Flowcast, an AI credit platform.

🚀 Traction

🏆 Competition

💰 Financials

🚩 Risks