Applied Computing Raises $20M to Build Foundation AI Model for Oil and Gas Industry

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

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. 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 or weeks into seconds, helping operators reduce energy use and maintain output. That promise of speed seems to have found believers. The startup says it has gone from stealth to double-digit millions in annual recurring revenue in under 18 months. Adamson said Orbital is in use at some "large, publicly listed" upstream oil and gas, downstream refining and petrochemicals companies, although he declined to mention how many customers it has. Its partners include Indian energy company Wipro, and KBR, which has integrated Orbital into its INSITE 3.0 digital platform for energy projects, and is using the product for ammonia production. Adamson said 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. Still, Applied Computing is entering a market that has entrenched industrial software suppliers as well as more focused AI startups. AspenTech sells simulation and AI-powered modeling software for upstream, refining and chemical operations, while AVEVA offers physics-based process simulation, optimization, and "what-if" modeling for industrial plants. Cognite and Seeq target the data layer, helping facilities analyze industrial data, and apply AI to design workflows. Adamson argues that the company's moat is not access to industrial data or process knowledge, but assembling AI researchers to build a model that can compete 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." Adamson also pointed to the data Orbital receives through its deployments. Operational data from refineries and other energy facilities is generally not available publicly, he said, while simulated data cannot fully reproduce what happens inside a working plant. The KBR partnership may help the company, too. Adamson said the partnership gives Applied Computing access to operational data, industry expertise, and also introductions to more potential customers. Applied Computing plans to use the $20 million to expand internationally, hire for research and engineering roles, and explore deployments with energy clients. The company on Thursday said it's also opened an office in Houston, adding to its headquarters in London and operational hub in Bengaluru. Adamson said the U.S. base puts the startup closer to two existing customers in North America, and an expansion into the Middle East is also in the works.

Applied Computing raises $20m to build a foundation model for the refinery

Plants collect thousands of sensor readings and act on less than 8% of them. The pitch is not more data. It is getting three kinds of it to talk. A single refinery can carry thousands of sensors measuring temperature, pressure, velocity, and viscosity. According to Applied Computing, operators make decisions using less than 8% of what those sensors tell them. The London startup has raised a $20m Series A to close that gap, led by engineering giant KBR with Databricks Ventures participating. Founded in 2023, it is building a foundation model for oil, gas, refining, and petrochemicals. The problem is not collection, according to co-founder and chief executive Callum Adamson. Operators already gather the information. They cannot combine sensor readings, engineering documentation, and the underlying physics and chemistry fast enough to predict anything useful from them. The shape will be familiar: a foundation model trained on proprietary industrial data, with a large incumbent as both investor and route to market. Mistral launched its industrial engineering tier with Airbus, BMW, and EDF as named customers on the same logic. "It's getting those three data sources to talk to each other in real time," Adamson told TechCrunch. "That's the real key." Its model, Orbital, is not a language model with an industrial skin on it. The company says it fuses a time series model, a physics-based model, and a language model to predict the state of a facility, reading sensor data while accounting for chemistry, equipment constraints, and what the operators are actually doing. It also lets technicians simulate how a change in one part of a plant would ripple through the rest. That is the part the industry has historically paid consultants and weeks of downtime for. It is also where the stakes sit. A refinery is not a customer-service queue, and Amazon has already warned that human oversight of AI degrades precisely because people stop scrutinising a system that is usually right. The pitch, in the end, is speed. Applied Computing claims Orbital can flag an anomaly, work out what caused it, and model whether a proposed fix creates a problem somewhere else, all within minutes. Adamson says investigations that took days or weeks compress into seconds. Some of this is landing. The company says it went from stealth to double-digit millions in annual recurring revenue in under 18 months, with Orbital deployed at unnamed "large, publicly listed" upstream, refining, and petrochemical companies. Adamson declined to say how many customers it has, which is the sort of omission worth noticing next to a revenue claim. KBR has integrated Orbital into its INSITE 3.0 platform and is using it for ammonia production. Adamson said the company is working with a major US upstream operator and expects to announce a European oil major in coming weeks. The competitive picture is crowded and old. AspenTech sells simulation and AI-powered modelling across upstream, refining, and chemicals, while AVEVA does physics-based process simulation and what-if modelling. Cognite and Seeq work the data layer. None of these are startups that can be outrun. Adamson's answer is that none of them are competing for the right talent. "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." It is a good line, and it is also the entire bet. The claim is that the moat is neither industrial data nor process knowledge, both of which the incumbents have in depth, but the ability to assemble researchers who can build a model that beats Orbital. Whether a $20m Series A buys that against AspenTech's installed base is the open question. There is a second-order argument underneath. Adamson notes that operational data from working refineries is not public, and that simulated data cannot reproduce what happens inside a live plant, which makes deployments themselves the asset. The KBR partnership matters for the same reason: it brings operational data, industry expertise, and introductions. That reasoning is why heavy industry keeps ending up here. UPS is running a real-time digital twin of its entire logistics network on much the same basis. The money goes on international expansion and research and engineering hires. The company opened a Houston office on Thursday, adding to its London headquarters and Bengaluru operational hub. The Middle East is next.