How Applied Computing's physics-informed AI is transforming heavy industry operations.

Applied Computing Raises $20M to Build an AI Foundation Model for Oil & Gas Operations:

How Orbital's Physics-Informed AI Is Closing the Industrial Data Gap in Energy Operations.

Industrial AI goes mainstream: Applied Computing raises $20M for operational physics.

$20M: Series A Led by KBR

<8%: Of Facility Data Used Today

18 mo: Stealth to Double-Digit ARR

1: The Problem: Oil & Gas Facilities Are Drowning in Untapped Data:

A single oil, gas, or petrochemical facility can carry thousands of sensors — and still make decisions blind.

London-based startup Applied Computing has raised a $20 million Series A led by engineering giant KBR, with participation from Databricks Ventures, to tackle a problem that sounds almost paradoxical: energy facilities are swimming in sensor data yet operating with almost none of it.

Founded in 2023, the company says operators currently make decisions using less than 8% of the data available to them, because sensor readings, engineering documentation, and the underlying physics and chemistry of a facility rarely talk to each other in real time.

Co-founder and CEO Callum Adamson describes it as a synchronization problem more than a data-collection one — the raw numbers already exist, but combining them fast enough to act on is where operators get stuck.

2: Inside Orbital: A Foundation Model Built for Physics, Not Just Text:

Orbital isn't a chatbot — it's a system built to predict the state of an entire facility.

Where large language models are built to predict the next word, Applied Computing's foundation model, Orbital, is designed to predict the next state of a physical plant. It fuses a time-series model, a physics-based model, and a language model, layering in equipment constraints and operator activity so it can flag anomalies, trace their root cause, and simulate whether a proposed fix might create new problems elsewhere in the facility — reportedly within minutes rather than the days or weeks such investigations traditionally take.

It's an AI problem. It's not a data problem, and it's not an energy problem. — Callum Adamson, Co-Founder & CEO, Applied Computing.

3: From Stealth to Scale: Partnerships With KBR, Wipro, and Beyond:

Applied Computing went from stealth to double-digit-million-dollar annual recurring revenue in under 18 months.

The company's traction already includes deployments with large, publicly listed upstream, downstream, and petrochemical operators, alongside named partners Wipro and KBR — the latter having integrated Orbital into its INSITE 3.0 digital platform for ammonia production.

Applied Computing also says it's working with a major U.S. upstream operator and expects to announce a European oil major partnership soon. The fresh capital will fund international expansion, additional research and engineering hires, and further energy-sector deployments, alongside a newly opened Houston office that joins its London headquarters and Bengaluru operations hub.

4: A Crowded Market — and Applied Computing's Bet on AI Talent:

Incumbents like AspenTech and AVEVA already sell simulation software to this industry — so what's the moat?

Applied Computing enters a market with entrenched players: AspenTech offers simulation and AI-powered modeling for upstream, refining, and chemical operations; AVEVA provides physics-based process simulation and optimization; and Cognite and Seeq focus on the industrial data layer.

Adamson argues the real differentiator isn't data access but talent — betting that top AI researchers will choose to build at an AI-native startup rather than inside a traditional energy company. Operational data from live deployments, which he says can't be fully replicated through simulation alone, is the other piece of the moat, reinforced by the access and industry expertise that comes with the KBR partnership.

The Takeaway for Enterprise Leaders:

Applied Computing's raise is another signal that the market is moving away from generic language models and toward purpose-built, physics-aware AI that understands the operating reality of a specific industry. Oil, gas, and petrochemical operators aren't alone in facing fragmented data, siloed systems, and slow decision cycles — the same challenge shows up across manufacturing, logistics, healthcare, and finance. That's exactly the gap Otherworlds AI's Agent+ Business AI Platform was built to close.

Powered by Google Opal automated workflows, Agent+ gives businesses an accessible, enterprise-grade AI layer — connecting data sources, automating decision workflows, and surfacing insights in real time, without the multi-year build cycle of a foundation model. Custom enterprise AI builds are also available for organizations with more specialized operational needs.

Ready to put your own operational data to work? Explore Agent+ at otherworldsai.com — starting at $297/month.