The energy industry has a dirty secret: refineries and petrochemical plants make operating decisions using less than 8% of the data their own sensors collect. Applied Computing, a London-based startup founded in 2023, just raised a $20 million Series A to fix that — with KBR leading the round and Databricks Ventures participating.
The company’s foundation model, Orbital, isn’t another ChatGPT wrapper. It combines a time series model, a physics-based model, and a language model to predict a facility’s state in real time. CEO Callum Adamson says the real challenge is getting sensor readings, engineering documentation, and chemistry to talk to each other simultaneously. “It’s getting those three data sources to talk to each other in real time. That’s the real key,” he told TechCrunch. The pitch is pure speed: investigations that once took days or weeks now take seconds, helping operators cut energy use while maintaining output.
That promise appears to be landing. Applied Computing says it went from stealth to double-digit millions in annual recurring revenue in under 18 months. Orbital is already deployed at unnamed “large, publicly listed” upstream and downstream companies. KBR has integrated the model into its INSITE 3.0 digital platform for ammonia production. The startup also works with Indian energy firm Wipro and plans to announce a European oil major partnership soon.
Adamson is betting the company’s moat isn’t data access — it’s talent. “If you’re a tier-one AI researcher, where are you going to work? I don’t think Shell’s on that list,” he said. The $20 million will fund international expansion, including a new Houston office and a planned push into the Middle East. Whether Orbital can outmaneuver entrenched players like AspenTech and AVEVA remains an open question, but Applied Computing is betting that assembling top AI researchers gives it an edge industrial giants can’t easily replicate.
