A London-based startup aiming to bring artificial intelligence to the heart of oil, gas and petrochemical operations has closed a $20m (£15.5m) Series A funding round. Applied Computing, founded in 2023, is building a foundation model called Orbital that ingests thousands of sensor readings — temperature, pressure, velocity, viscosity — alongside engineering documents and physics principles to give operators a real-time view of an entire facility.
According to co-founder and CEO Callum Adamson, the industry currently makes operational decisions using less than 8% of the data it collects. The challenge, he says, is not a lack of information but the difficulty of combining sensor streams, engineering documentation and chemical physics quickly enough to generate predictions. “It’s getting those three data sources to talk to each other in real time. That’s the real key,” he told TechCrunch.
Orbital differs from large language models by blending a time series model, a physics-based model and a language model. It can flag anomalies, investigate root causes and simulate the knock-on effects of a proposed fix — all within minutes. Adamson claims the system compresses analyses that previously took days or weeks into seconds, helping operators cut energy use while sustaining output. The startup says it has gone from stealth to double-digit millions in annual recurring revenue in under 18 months.
The Series A was led by KBR, an engineering and construction firm that has already integrated Orbital into its INSITE 3.0 digital platform for energy projects, including ammonia production. Databricks Ventures also participated. Applied Computing is working with Indian energy company Wipro, a “major US upstream operator” and plans to announce a partnership with a European oil major shortly.
Competition in the industrial AI space is fierce. AspenTech, AVEVA, Cognite and Seeq all offer simulation, data analysis or AI-powered modelling for energy facilities. Adamson argues that Applied Computing’s moat is not proprietary data but its ability to attract tier-one AI researchers. “It’s an AI problem. It’s not a data problem, and it’s not an energy problem,” he said, adding that operational data from real plants gives Orbital an edge over models trained on simulated data alone.
For UK businesses and regulators, the rise of specialised industrial AI raises questions about data governance and model transparency. The Information Commissioner’s Office (ICO) has yet to issue specific guidance on foundation models in critical infrastructure, while the EU AI Act classifies systems used in energy and chemical plants as high-risk. Experts caution that while AI-driven efficiency could lower operational costs and emissions, reliance on opaque models in safety-critical environments demands robust oversight and explainability standards.