Physics-Informed AI Aims to Modernize Oil and Gas Plant Operations with $20M Series A

Applied Computing has raised a $20 million Series A funding round to build a foundation AI model for the oil, gas, and petrochemical industry, with the goal of replacing decades-old rule-based software that has governed refinery operations since the 1970s. The round was led by KBR, with Databricks Ventures also participating, according to TechCrunch and EU-Startups.

2 sources

TechCrunchApplied Computing wants to give oil and gas operators an AI model for the entire plantEU-StartupsLondon-based Applied Computing raises €17.4 million to scale AI that works for the energy industry

The London-based company is simultaneously expanding into the United States, opening a new office in Houston, Texas, as reported by EU-Startups and UKTN. Its flagship platform, Orbital, is described as the first foundation model built specifically for energy operations, combining physics-grounded intelligence.

UKTNApplied Computing raises £14.8m to scale AI for energy operators

"decades-old, fragmented rule-based software that has governed refinery operations since the 1970s"

fathom.newsApplied Computing Raises $20M to Build AI Foundation Model for Oil and Gas Plant Operations

Applied Computing aims to bring a modern AI-driven approach to industrial plant operations, moving beyond the fragmented systems that operators have relied on for half a century.

"the first foundation model built specifically for energy operations"

According to UKTN, Orbital is tailored to the energy sector, integrating physics-based data to improve decision-making across entire plants. The funding will scale this model, which Superintelligence News noted is backed by KBR and Databricks Ventures.

Superintelligence NewsApplied Computing Raises $20M to Build an AI Model for Oil and Gas Plants

With the Series A and U.S. expansion, Applied Computing is positioning itself to transform how oil and gas operators manage and optimize their facilities, offering a unified AI alternative to legacy control systems.