Applied Computing, a London-based startup that’s building a foundation AI model for the oil, gas and petrochemical industry, has raised a $20 million Series A led by engineering giant KBR, with Databricks Ventures participating.
Founded in 2023, the startup 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. While there’s a huge market for helping energy companies solve the data tracking problem, the fragmentation that presents a significant hurdle. Facilities consequently make operating decisions using less than 8% of the data available to them, says Applied Computing’s co-founder and CEO Callum Adamson (pictured above, right). 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.Unlike large language models, which predict the next word, Applied Computing says its foundation model, Orbital, 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.
Image Credits:Applied Computing / Applied Computing
Essentially, Applied Computing is pitching speed: It claims Orbital can flag anomalies, investigate what caused them, and model whether a proposed fix could create problems elsewhere in the facility, all within minutes. Adamson claims the product can compress investigations that previously took days …