London-based startup Applied Computing has secured $20 million in Series A funding to scale its foundation AI model built specifically for the oil, gas, and petrochemical industry. The round was led by engineering giant KBR, with Databricks Ventures also participating.
Founded in 2023, the company has developed a model called Orbital that aims to solve a persistent challenge in energy facilities: making sense of the vast amounts of data generated by thousands of sensors while accounting for the complex physics and chemistry governing plant operations.
The Data Problem Plaguing Energy Facilities
Modern oil, gas, refining, and petrochemical facilities are equipped with thousands of sensors measuring variables such as temperature, pressure, velocity, and viscosity. Yet despite this wealth of information, plant operators make decisions using less than 8% of the data available to them, according to Applied Computing co-founder and CEO Callum Adamson.
The issue, Adamson explained, is not a lack of data collection. Operators already gather much of this information. The real challenge lies in combining sensor readings, engineering documentation, and physics and chemistry principles quickly enough to analyze and make predictions in real time.
"It's getting those three data sources to talk to each other in real time. That's the real key," Adamson told TechCrunch.
How Orbital Combines Multiple AI Approaches
Unlike large language models that predict the next word in a sequence, Applied Computing's foundation model takes a multi-layered approach. Orbital integrates a time series model, a physics-based model, and a language model to predict the state of a facility.
The system works by analyzing sensor readings while keeping physics and chemistry constraints in mind. It also recognizes a facility's equipment limitations and operator activity patterns. Beyond monitoring, Orbital allows technicians to run simulations to see how a change in one part of a facility might ripple through the rest of its operations.
The company's core pitch centers on speed. Applied Computing claims Orbital can flag anomalies, investigate their root causes, and model whether a proposed fix could create problems elsewhere in the facility — all within minutes. Adamson said the product can compress investigations that previously took days or weeks into seconds, helping operators reduce energy consumption while maintaining output.
Rapid Traction in a Crowded Market
Applied Computing's value proposition appears to be resonating with the industry. The startup reports it has gone from stealth mode to double-digit millions in annual recurring revenue in under 18 months. Orbital is currently in use at several large, publicly listed companies spanning upstream oil and gas, downstream refining, and petrochemicals, though Adamson declined to disclose the exact number of customers.