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Home GREEN

London-based Applied Computing raises €10.7 million to bring AI to the energy industry

EU Startupsby EU Startups
May 28, 2025
Reading Time: 3 mins read
in GREEN, UK&IRELAND
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British AI company Applied Computing, focused on foundation models built specifically for the Oil, Gas and Petrochemical Industries, announces today a €10.7 million Seed funding round.

The round was led by Stride.VC with Repeat.vc. The raise is reportedly among the largest ever for a UK-based AI firm at Seed stage.

“Refining is where complexity, scale, and impact converge. It’s not just hard – it’s the apex of validation. If you can build intelligence that works here, it will work anywhere,” said Callum Adamson, CEO of Applied Computing. “Orbital is already delivering at production scale in some of the most complex industrial environments on the planet. It’s improving margins, reducing emissions and laying the groundwork for a fundamentally more intelligent energy system. I couldn’t be prouder of the team, and this is just the beginning.”

Founded in 2023 by Callum Adamson (CEO) and Dr Sam Tukra (Chief AI Officer), Applied Computing is working with complex industrial sites and even large oil refineries thanks to their flagship product: Orbital.

Orbital is built using multi-foundation AI. It’s powered by a new class of models built to optimise the physical world – not just language models but also time series, physics and chemical engineering models delivering explainable AI that can be trusted in real-world applications – according to Applied Computing.

Orbital utilises 100% of available data from downstream energy facilities – compared to 8% captured by traditional methods – and is reportedly outperforming previously benchmarked state-of-the-art software by 90% in key metrics.

Applied Computing is developing this advanced AI for the heavy industries essential to human life, primarily oil, gas and petrochemicals.

“There is a comforting fiction that we can simply swap out the old for the new,” said Dr Sam Tukra, Chief AI Officer. “But the scale of the transition is immense. The infrastructure of heavy industry cannot be erased – it must be transformed. That’s why we built Orbital. Without the energy industry, life as we know it is impossible. Refiners and petrochemical firms are the lifeblood of civilisations, yet their existence is frozen in time as many of the facilities that power our world are nearly 50 years old. As energy and chemical demand rises steeply at a global level, it is crucial that these essential industries transform themselves. Artificial intelligence can deliver significant financial and environmental improvements to benefit everyone on the planet.”

Applied Computing offers a grounded path forward: using AI to make existing energy infrastructure more efficient, secure and climate-aligned.

In live deployments, Orbital is delivering an estimated 75% cost saving versus traditional cloud-based data platforms, with a payback period of under 10 weeks. By combining foundation models in language, physics, chemical engineering and time series forecasting, and deploying fully on-edge, Orbital provides real-time intelligence that improves margins – without sending sensitive operational data offsite.

The company has doubled in size since January and is preparing for a Series A in H2 2025.

Fred Destin, founder of Stride.VC, commented: “Beyond LLM hype, companies such as Applied Computing are trailblasing a profound transformation in the efficiency with which our most critical industries are run, starting with fossil fuels, and demonstrating the promise of responsible Artificial Intelligence in driving profound positive change for humans and the planet. Small bands of highly competent rebels like Sam, Callum and the Applied Computing team show what is possible in transforming huge industries like Energy and Petrochemicals at speed. What is more, this is proudly Built in Britain“.

Read the orginal article: https://www.eu-startups.com/2025/05/london-based-applied-computing-raises-e10-7-million-to-bring-ai-to-the-energy-industry/

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