Applied Computing wants to give oil and gas operators an AI model for the entire plant

Applied Computing, a London-based artificial intelligence startup, has closed a $20 million Series A funding round to accelerate development of a foundation AI model built specifically for the oil, gas and petrochemical sector. The round was led by KBR, the global engineering and project management firm, with additional backing from Databricks Ventures, the investment arm of the data and AI company Databricks.

The startup's core product, called Orbital, is designed to act as a kind of unified intelligence layer across an entire industrial facility rather than a single piece of equipment or process line. Instead of monitoring isolated systems in silos, the model is meant to ingest signals from across a plant so operators can understand how issues in one area might ripple into others.

According to the company, Orbital's central selling point is speed. The system is built to detect operational anomalies, trace their root causes, and simulate whether a proposed corrective action might trigger new problems elsewhere in the facility — a process the company says can be completed in a matter of minutes rather than the days or weeks such investigations traditionally require.

Company executive Adamson said the tool is intended to help refiners and producers cut down on wasted energy while keeping output steady, framing the technology as a way to shrink the gap between spotting a fault and fixing it. That kind of rapid diagnosis has long been a bottleneck in heavy industrial operations, where plants often rely on engineers manually cross-referencing sensor data, maintenance logs and safety protocols before authorizing changes.

Applied Computing says the pitch is already resonating commercially. The company reports it moved from operating in stealth to generating tens of millions of dollars in annual recurring revenue in less than a year and a half — a rapid trajectory for a vertical-specific industrial AI product. Adamson said Orbital has been adopted by several large, publicly traded companies spanning upstream exploration and production, downstream refining, and petrochemical manufacturing, though he did not disclose the number of customers or name specific clients.

The investment adds to a broader wave of interest in applying large-scale AI models to heavy industry, a sector that has historically lagged behind software and consumer markets in AI adoption due to safety requirements, legacy infrastructure and the high cost of errors. KBR's decision to lead the round is notable given its own deep ties to engineering and construction work for energy producers, suggesting the firm sees strategic value in embedding AI-driven diagnostics into the plants it helps design and build.

Still, unanswered questions remain about how the technology performs at scale. Applied Computing has not detailed how Orbital's models are trained, how they are validated for accuracy in safety-critical environments, or how the company handles the proprietary operational data of the industrial clients it serves. As with many emerging industrial AI tools, broader adoption may hinge on whether operators — and regulators overseeing plant safety — are willing to trust automated systems with recommendations that could affect physical infrastructure and worker safety, not just software outcomes.

Source: TechCrunch