Applied Computing raises $20M to build an AI model that runs entire oil and gas plants

Applied Computing, a London-based startup developing a foundation AI model for the oil, gas, and petrochemical industry, has raised $20 million in Series A funding led by engineering firm KBR, with participation from Databricks Ventures. The company aims to help operators use the vast amounts of sensor data generated by industrial plants to make faster, more informed decisions.

Why this matters for the energy industry

Oil, gas, and petrochemical facilities can have thousands of sensors measuring variables such as temperature, pressure, velocity, and viscosity. Yet operators currently use less than 8% of that data when making operational decisions, according to Applied Computing co-founder and CEO Callum Adamson. The core challenge is not collecting data, but combining sensor readings with engineering documentation and physics-based models in real time. Applied Computing says its foundation model, called Orbital, is designed to bridge that gap.

How Orbital works

Unlike large language models that predict the next word, Orbital combines a time series model, a physics-based model, and a language model to predict the state of a facility. It analyzes sensor readings while accounting for physical and chemical constraints, equipment limitations, and operator activity. The system can also run simulations to show how a change in one part of a plant might affect other operations elsewhere.

According to Adamson, Orbital can flag anomalies, investigate their root causes, and model whether a proposed fix could create problems elsewhere — all within minutes. He claims the product compresses investigations that previously took days or weeks into seconds, helping operators reduce energy use while maintaining output.

Market traction and competition

Applied Computing says it has gone from stealth to double-digit millions in annual recurring revenue in under 18 months. While Adamson declined to name specific customers, he said Orbital is in use at large, publicly listed upstream oil and gas, downstream refining, and petrochemical companies. The startup has partnerships with Indian energy firm Wipro and KBR, which has integrated Orbital into its INSITE 3.0 digital platform for energy projects. Adamson also noted that the company is working with a major U.S. upstream operator and expects to announce a partnership with a European oil major in the coming weeks.

The startup enters a competitive market that includes established industrial software suppliers such as AspenTech and AVEVA, as well as data-focused AI startups like Cognite and Seeq. Adamson argues that Applied Computing’s moat lies not in access to industrial data or process knowledge, but in its ability to attract top AI researchers to build a model that competes with Orbital. Operational data from refineries is generally not publicly available, he said, and simulated data cannot fully replicate real plant conditions — giving the startup an advantage as it collects proprietary data through deployments.

Plans for the funding

Applied Computing plans to use the $20 million to expand internationally, hire for research and engineering roles, and explore additional deployments with energy clients. The company has opened an office in Houston, adding to its headquarters in London and operational hub in Bengaluru. Adamson said the U.S. base will help the startup work more closely with two existing North American customers, and an expansion into the Middle East is also underway.

Conclusion

Applied Computing’s approach addresses a long-standing inefficiency in the energy sector: the inability to use the majority of operational data to optimize plant performance. With KBR’s backing and a growing customer base, the startup is positioning itself as a key player in the intersection of AI and industrial operations. The success of its model will depend on whether it can continue to differentiate itself from both legacy software providers and emerging AI competitors.

FAQs

Q1: What does Applied Computing’s AI model do?
Orbital combines time series, physics-based, and language models to analyze sensor data from oil, gas, and petrochemical plants, helping operators detect anomalies, simulate changes, and optimize operations in real time.

Q2: How much funding has Applied Computing raised?
The company raised $20 million in a Series A round led by KBR, with Databricks Ventures participating. The funding was announced in early November 2025.

Q3: Who are Applied Computing’s competitors?
Competitors include AspenTech, AVEVA, Cognite, and Seeq, which offer simulation, data analysis, and AI-powered modeling for industrial operations.