Applied Computing has secured $20 million in Series A funding led by engineering firm KBR, with Databricks Ventures joining the round. The London-based startup is building a foundation AI model for the oil, gas, and petrochemical sectors.
The data gap
Founded in 2023, the company focuses on systems where a single facility hosts thousands of sensors tracking temperature, pressure, velocity, and viscosity. Despite the volume of information, facilities rely on less than 8% of available data for operating decisions, according to co-founder and CEO Callum Adamson.
Operators collect this information but struggle to merge sensor readings with engineering documentation and physics and chemistry fast enough to make predictions. “It’s getting those three data sources to talk to each other in real time. That’s the real key,” Adamson told TechCrunch.
How Orbital works
Applied Computing’s model, Orbital, differs from standard large language models that predict the next word. Instead, it combines a time series model, a physics-based model, and a language model to forecast a facility’s state. The system analyses sensor data while accounting for chemical constraints and operator activity. It also lets technicians simulate how changes in one area affect the rest of the plant.
The pitch is speed. Orbital claims it can flag anomalies, identify causes, and model potential side effects of a proposed fix within minutes. Adamson says the product compresses investigations that previously took days or weeks into seconds, helping operators cut energy use while maintaining output.
Adoption and partners
The startup moved from stealth to double-digit millions in annual recurring revenue in under 18 months. Orbital is currently used by some large, publicly listed upstream oil and gas, downstream refining, and petrochemicals companies, though Adamson declined to specify the number of customers.
Partners include Indian energy firm Wipro and KBR, which has integrated Orbital into its INSITE 3.0 digital platform for energy projects. KBR is using the tool for ammonia production. The startup is also working with a major U.S. upstream operator and plans to announce a partnership with a European oil major in the coming weeks.
Applied Computing has opened an office in Houston, joining its headquarters in London and operational hub in Bengaluru. Adamson noted the U.S. base brings the team closer to two existing North American customers. Expansion into the Middle East is also in progress.
Competition and defences
Applied Computing faces established industrial software suppliers and focused AI startups. AspenTech sells simulation and AI-powered modeling software for upstream, refining, and chemical operations. AVEVA offers physics-based process simulation, optimisation, and “what-if” modelling for industrial plants. Cognite and Seeq target the data layer, helping facilities analyse industrial data and apply AI to design workflows.
Adamson argues the company’s advantage is not access to industrial data or process knowledge, but the ability to assemble AI researchers to build a model that competes 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.”
Operational data from refineries and other energy facilities is generally not available publicly, Adamson noted, while simulated data cannot fully reproduce conditions inside a working plant. The KBR partnership provides access to operational data, industry expertise, and introductions to potential customers.
The company plans to use the funding to expand internationally, hire for research and engineering roles, and explore further deployments with energy clients.
What it means
For plant operators, the shift means faster decision-making cycles. Instead of waiting days to understand a problem or model a fix, teams get immediate feedback. This allows for quicker adjustments to energy consumption and output without waiting for slow, manual analysis of disparate data sources.
