Applied Computing wants to give oil and gas operators an AI model for the entire plant
Quick Answer
Applied Computing has raised $20 million for its AI model, Orbital, designed to optimize operations in the oil and gas sector by integrating real-time sensor data, physics, and chemistry.
Quick Take
Applied Computing has raised $20 million for its AI model, Orbital, designed to optimize operations in the oil and gas sector by integrating real-time sensor data, physics, and chemistry. The model aims to enhance decision-making efficiency, reducing investigation times from days to seconds, and is already generating double-digit millions in annual recurring revenue within 18 months.
Key Points
- Orbital combines time series, physics-based, and language models for facility predictions.
- The startup's revenue reached double-digit millions within 18 months of launching.
- Applied Computing plans to expand internationally and hire for research roles.
- Partnerships with KBR and Wipro enhance access to operational data and industry expertise.
- The company is entering a competitive market with established industrial software suppliers.
📖 Reader Mode
~4 min readApplied 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.
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Ram is a financial and tech reporter and editor. He covered North American and European M&A, equity, regulatory news and debt markets at Reuters and Acuris Global, and has also written about travel, tourism, entertainment and books.
You can contact or verify outreach from Ram by emailing ram.iyer@techcrunch.com.
— Originally published at techcrunch.com
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