- Gigaton (formerly Carbon Re) has raised $26M in a Series A led by Plural to scale AI-powered autonomous control systems for cement, steel, glass, and chemicals plants.
- Customers, including Adani Cement, Heidelberg Materials, Holcim, and Mannok Holdings, are already saving $1M–$3M annually per plant while cutting approximately 30,000 tonnes of CO₂ per year.
- Funding will support a fivefold team expansion and entry into steel, glass, and chemicals manufacturing.
For decades, heavy industry has relied on control systems designed for a different era. Today, manufacturers face soaring energy costs, volatile fuel markets, tightening emissions regulations, and growing operational complexity, yet many cement plants, steel mills, and glass factories still run on software built decades ago, requiring constant human intervention to maintain efficiency. Meanwhile, China is accelerating toward fully autonomous “dark plants” capable of operating with minimal on-site oversight, widening the competitive gap for industrial producers elsewhere.
London-based Gigaton believes AI can help close it. The company has raised $26M in Series A funding led by Plural along with 2150, Semapa Next, Planet A Ventures, Cambridge Enterprise Ventures, UCL Technology Fund managed by AlbionVC with UCL Business, and Clean Growth Fund. The round brings total funding to more than $35M and will be used to grow headcount fivefold and expand beyond cement into steel, glass, and chemicals.
From university spinout to industrial AI challenger
Gigaton was founded in 2020 as Carbon Re, the first joint spinout from both University College London and the University of Cambridge, by Dr Daniel Summerbell, Buffy Price, Sherif Elsayed-Ali, and Dr Aidan O’Sullivan. In early 2024, experienced entrepreneur Josh Vernon was appointed CEO, relocating from Sydney to lead the company’s next growth phase.
Rather than building another analytics dashboard, the team spent years working directly with operators inside industrial control rooms to understand why existing systems fail to adapt to modern production demands. That research led to AI software capable of simulating plant behaviour, forecasting outcomes, and autonomously adjusting operational parameters, fuel mix, oxygen levels, and kiln speeds in real time.
Unlike most industrial AI solutions that sit atop existing infrastructure, Gigaton is designed to replace parts of the underlying control software itself. The result is lower fuel consumption, reduced emissions, greater operational stability, and improved plant performance across facilities already in production.
Solving heavy industry’s energy and carbon challenge
Energy-intensive industries account for a significant share of global emissions and energy consumption. Gigaton’s platform addresses both cost and carbon simultaneously. Deployments with customers, including Adani Cement, Heidelberg Materials, Holcim, and Mannok Holdings, generate annual savings of between $1M and $3M per plant while reducing emissions by approximately 30,000 tonnes of CO₂ annually.
“We have built Gigaton to deliver real cost and carbon savings now while building the AI infrastructure these industries need in a fully autonomous future,” said Vernon.
Competition
Gigaton operates within a fast-growing industrial AI and climate technology sector. In materials science, Boston Metal has raised $282 million across its Series C rounds to commercialise Molten Oxide Electrolysis for emissions-free steel production. Electra raised $186M in a Series B co-led by Capricorn Investment Group and Temasek Holdings in April 2025 to decarbonise iron production using low-temperature electrochemical processes. Both target new production methods rather than optimising existing infrastructure.
What’s next?
Heavy industry consumes roughly a quarter of global energy, yet many facilities still depend on ageing software and manual processes. As energy costs rise and carbon regulations tighten, autonomous control systems are moving from a competitive advantage to an operational necessity. The question for Gigaton is whether the same AI architecture that works in cement kilns can transfer fast enough to steel furnaces and glass lines before better-capitalised competitors fill the same gap from above or below.
