Applied Computing, a London-based startup, has raised $20 million in Series A funding to develop Orbital, an AI model designed to help oil and gas operators analyze data from entire plants. Founded in 2023, the company targets oil, gas, refining, and petrochemical systems, where a single facility can have thousands of sensors measuring everything from temperature and pressure to velocity and viscosity. Applied Computing’s co-founder and CEO, Callum Adamson, said facilities make operating decisions using less than 8% of the data available to them. Operators already collect much of this information, he said, but they struggle to combine the sensor readings, engineering documentation, and physics and chemistry quickly enough to analyze and make 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, the startup’s foundation model, combines a time series model, a physics-based model, and a language model to predict the state of a facility. It does this by analyzing sensor readings, keeping physics and chemistry in mind, and recognizing a facility’s equipment constraints and operator activity. It also allows technicians to run simulations of how a change in one part of a facility could affect the rest of its operations. Adamson claims the product can compress investigations that previously took days or weeks into seconds, helping operators reduce energy use and maintain output. The startup says it has gone from stealth to double-digit millions in annual recurring revenue in under 18 months.
Applied Computing is entering a market with entrenched industrial software suppliers, as well as more focused AI startups. AspenTech sells simulation and AI-powered modeling software for upstream, refining, and chemical operations, while AVEVA offers physics-based process simulation, optimization, and "what-if" modeling for industrial plants. Cognite and Seeq target the data layer, helping facilities analyze industrial data, and apply AI to design workflows. Adamson argues that the company’s moat is not access to industrial data or process knowledge, but rather assembling AI researchers to build a model that can compete with Orbital. "It’s an AI problem. It’s not a data problem, and it’s not an energy problem," he said. "If you’re a tier-one AI researcher, where are you going to work? … I don’t think Shell’s on that list."
Source: techcrunch
