Applied Computing’s AI model, Orbital, helps oil and gas plants analyze sensor data, physics, and chemistry in real time to predict facility states and flag anomalies in minutes. The London-based startup just raised $20 million to scale this technology globally.
How Orbital’s AI model works for oil and gas
Think of Orbital as a supercharged translator for industrial plants. It combines three data streams—sensor readings, engineering docs, and physics/chemistry rules—to simulate how changes in one part of a facility ripple through the rest. Unlike typical AI that predicts text, Orbital predicts the state of a plant, compressing investigations from days to seconds.
Today, most energy facilities use less than 8% of their available data for decisions, despite thousands of sensors tracking temperature, pressure, and more. Orbital’s edge? It merges these fragmented data sources in real time, letting operators test fixes virtually before applying them.
Competition and market traction
Applied Computing isn’t alone in industrial AI. Competitors like AspenTech and AVEVA offer simulation and optimization tools, while Cognite and Seeq focus on data analysis. But CEO Callum Adamson argues Orbital’s advantage is its AI-first approach: “It’s an AI problem, not a data problem.”
The startup’s rapid growth—double-digit millions in annual recurring revenue in under 18 months—proves the demand. Partners include KBR (which integrated Orbital into its INSITE 3.0 platform) and Wipro, with deployments in upstream oil, refining, and petrochemicals. A U.S. office in Houston and Middle East expansion are next.
What’s next for Applied Computing
The $20 million Series A, led by KBR with Databricks Ventures participating, will fund international expansion, hiring, and new energy client deployments. Adamson also teases a partnership with a European oil major “in the coming weeks.”
