Applied Computing, a London-based startup building a foundation AI model for oil, gas, refining, and petrochemical plants, has raised a $20 million Series A led by engineering firm KBR, with Databricks Ventures participating. The three-year-old company says its model, Orbital, is already deployed at large publicly listed upstream and downstream operators, and that it has grown from stealth to double-digit millions in annual recurring revenue in under 18 months.
The pitch targets a data problem that has resisted every prior wave of industrial software. A single refinery or petrochemical facility runs thousands of sensors measuring temperature, pressure, velocity, and viscosity, yet operators make decisions using less than 8% of the data they collect, according to co-founder and CEO Callum Adamson. The bottleneck is not collection — it is fusing sensor streams, engineering documentation, and the underlying physics and chemistry fast enough to act.
Adamson frames the core technical bet plainly. Orbital is not a language model predicting the next word; it stacks a time-series model, a physics-based model, and a language model to predict the state of a facility, accounting for equipment constraints and operator activity. Technicians can run simulations to see how a change in one unit ripples through the rest of the plant.
“It's getting those three data sources to talk to each other in real time. That's the real key,”— Callum Adamson, Co-founder and CEO of Applied Computing
Key facts
- 01Applied Computing raised a $20M Series A led by engineering firm KBR, with Databricks Ventures participating.
- 02The London startup says operators run facilities using less than 8% of the sensor data they collect.
- 03Its Orbital model combines time-series, physics-based, and language models to predict plant state and simulate changes.
- 04Applied Computing says it went from stealth to double-digit-million ARR in under 18 months.
- 05KBR has integrated Orbital into its INSITE 3.0 platform and is using it for ammonia production.
The commercial claim is speed. Applied Computing says Orbital can flag an anomaly, trace its cause, and test whether a proposed fix creates new problems elsewhere in the facility within minutes, compressing investigations that previously took days or weeks. In an industry where an unplanned outage at a refinery can cost millions per day, that time compression is the wedge.
The customer roster is thin on names but heavy on category. Adamson said Orbital is in use at large, publicly listed upstream oil and gas producers and downstream refining and petrochemical firms, though he declined to disclose customer counts. Named partners include Indian energy firm Wipro and KBR, which has integrated Orbital into its INSITE 3.0 digital platform and is applying it to ammonia production. A partnership with a European oil major is expected in the coming weeks, alongside work with a US upstream operator.
Applied Computing is walking into a market with entrenched incumbents. AspenTech sells simulation and AI-driven modeling software across upstream, refining, and chemicals. AVEVA offers physics-based process simulation and what-if modeling for industrial plants. Cognite and Seeq work the data layer, helping facilities analyze industrial data and layer AI onto existing workflows. Each has years of embedded contracts and integration inside the operators Applied Computing is chasing.
“It's an AI problem. It's not a data problem, and it's not an energy problem”— Callum Adamson, Co-founder and CEO of Applied Computing
Adamson's argument for defensibility is talent, not data. He contends the moat is assembling AI researchers capable of building a model that can compete with Orbital, on the reasoning that top-tier researchers are unlikely to choose an oil major as an employer. He also argues that operational data from working refineries is not publicly available and that simulated data cannot fully substitute for what a running plant produces, so early deployments compound Orbital's advantage over time.
The KBR relationship does double duty. It provides equity capital, but more importantly it hands Applied Computing operational data, domain expertise, and a warm channel to customers KBR already serves as an engineering contractor. Databricks Ventures on the cap table also signals where the data-layer plumbing is expected to live.
The Series A will fund international expansion, research and engineering hiring, and deeper deployments. The company opened a Houston office this week, putting it closer to two existing North American customers, and is planning a Middle East expansion. Headquarters remain in London, with an operational hub in Bengaluru.
The skeptical read is that industrial AI has been promised before, and refineries buy on reliability rather than novelty. Orbital's speed claims — investigations in seconds, simulations in minutes — will be tested against safety-critical workflows where a wrong recommendation carries real consequences. Incumbents like AspenTech and AVEVA have deep procurement relationships and certified integrations that a startup, however well-funded, does not replicate in a single sales cycle.
Still, the fact that KBR led the round rather than a generalist venture firm is the signal worth watching. Vertical foundation models for industrial systems are a bet that the same architectural stack powering chat products can be pointed at physical plants and yield equivalent returns. If Orbital's early ARR trajectory holds, Applied Computing will not need to displace AspenTech to matter — it will only need to prove that the model layer, not the software layer, is where the next decade of industrial software value accrues.
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