SINGAPORE, 08 April 2026 — Muun AI, an AI company building industrial intelligence systems, today announced a US$700,000 pre-seed round from Wavemaker Impact, Southeast Asia’s leading climate-tech venture build fund.
Founded by Kathryn Knight — a Silicon Valley AI veteran who holds three machine learning patents — Muun AI has developed a platform that reads data from industrial machines in real time, converting raw telemetry into ranked, confidence-scored operational insights. In a live proof of concept at a Singapore manufacturing facility, the platform identified between 2,800 and 4,200 hours of operational inefficiencies recoverable without changes to existing workflows.
Over the past two decades, companies across manufacturing, infrastructure, logistics, and commercial real estate have invested more than US$550 billion digitising their operations. Yet an estimated 50–80% of that data is never analysed or acted on, leaving a widening gap between the data companies hold and the intelligence they extract from it.
The climate cost of operational inefficiencies is also significant. In manufacturing alone, up to 40% of energy use is wasted. Across industry, transport, and buildings — sectors responsible for more than one-third of global emissions — AI-driven optimisation could reduce global emissions by up to 10%, equivalent to 3.8 GtCO₂e annually.
At the core of Muun AI’s platform is a proprietary industrial data-labelling engine that automatically labels and contextualises raw sensor data — temperature, pressure, timing — without requiring historical data or pre-training.
Machines designed to be identical rarely behave identically over time. Wear, calibration drift, and operating conditions cause assets to diverge, yet most facilities still manage fleets using fixed averages and standardised cycle times. Rather than managing fleets of machines using fixed averages, Muun AI establishes a performance baseline for each individual asset, allowing operators to optimise every machine to its true operating limits rather than the lowest-performing unit in a fleet.
The platform maps each data point to its role within the production cycle, surfaces operator-ready insights, and layers in predictive capabilities as it learns, including completion forecasts, anomaly detection, and full explainability tracing each output back to its source.
Every output is auditable — a requirement in environments where decisions must be trusted before they are acted on. Designed to be sector-agnostic, the platform is deployable across manufacturing, data centres, logistics, and energy systems. Each deployment builds a facility-specific intelligence layer that grows more accurate over time, creating proprietary operational data that cannot be easily replicated.
In practice, this allows facilities to move beyond “lowest-common-denominator” operations — where cycle times and maintenance schedules are set to the weakest asset — and instead manage each machine according to its actual performance and condition.
“Search engines did not create the internet’s information — they made it legible for the first time. We are building the same thing for the physical world. Every industrial operation is already generating the data that could transform how it runs. The results we are publishing today show that patterns invisible for years can be recovered directly from telemetry operators already have — with 96.6% accuracy at first contact.”
— Kathryn Knight, Founder & CEO, Muun AI
“Industry accounts for over 30% of global CO₂e emissions, and AI is the next wave of industrial transformation. While industrial sectors have spent decades digitising their operations, most machine data still goes unread. Muun AI sits on top of existing systems to convert that raw data into clear, actionable intelligence in real time, with no additional hardware or complex integration required. Kathryn combines deep technical expertise with a clear understanding of what it takes to solve this problem at scale.”
— Marie Cheong, Founding Partner, Wavemaker Impact
Muun AI is already running on live production data in an active Singapore manufacturing facility. With its pre-seed round closed, the company will expand its AI team and scale live engagements across manufacturing and other energy-intensive sectors, converting active pilots into long-term commercial partnerships.
A detailed white paper — expected in Q2 2026 — will outline the full technical results, optimisation opportunities, and quantified ROI across energy savings, throughput, and quality improvements.
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Media Contact
Kathryn Knight | Founder & CEO, Muun AI | kathryn@muun-ai.com
About Muun AI
Muun AI is a technology company that has built the first AI-native platform for physical operational intelligence — converting the raw machine telemetry that industrial operations generate continuously into agentic, explainable, and auditable decisions in real time. The platform’s proprietary data labelling engine achieves 96.6% cycle segmentation accuracy at first deployment, without training data, without new hardware, and without system integration — building a proprietary process-level intelligence asset that compounds with every engagement. Muun AI is backed by Wavemaker Impact. For more information, visit https://muun-ai.com.
About Wavemaker Impact
Wavemaker Impact is Southeast Asia’s leading climate-tech venture build fund, part of Wavemaker Partners. Launched in 2021, Wavemaker Impact co-founds sustainability startups with proven entrepreneurs, with the goal of building a portfolio of companies by 2035 that has the potential to reduce 10% of the global carbon budget. Every startup that Wavemaker Impact builds is a ‘100×100’ company — with the potential to abate 100 million metric tonnes of CO₂e and generate US$100 million revenue per year. Its US$60 million debut fund focuses primarily on Southeast Asia and includes limited partners such as the United States International Development Finance Corporation, British International Investment, Triple Jump, JG Summit, and Qarlbo Energy. For more information, visit https://wavemakerimpact.com.