Applied Computing, a London-based AI startup, has raised $20 million（約32億円） to expand its Orbital foundation model—a system that combines time-series analysis, physics-based modeling, and language understanding to help oil and gas facilities predict equipment behavior and detect problems.
The startup says the tool can compress investigation and analysis work that previously took days into seconds, and it is already generating double-digit millions in annual recurring revenue from large energy operators and has partnerships with KBR and other major companies.
What happened
Applied Computing, a London-based startup founded in 2023, raised $20 million（約32億円） in Series A funding led by KBR (an engineering company) and participated in by Databricks Ventures. The startup has built a foundation AI model called Orbital that helps oil, gas, refining, and petrochemical facilities analyze sensor data, engineering documentation, and physics/chemistry information to predict plant state and flag anomalies.
Why it matters
Energy operators currently make decisions using less than 8% of the data available to them, according to CEO Callum Adamson, because they struggle to combine sensor readings, documentation, and scientific knowledge quickly. Orbital compresses investigations that previously took days or weeks into seconds, potentially helping operators reduce energy use and maintain output. The startup has already grown to double-digit millions in annual recurring revenue in under 18 months and is working with large publicly listed upstream, downstream, and petrochemical companies.
What to watch
Applied Computing plans to use the $20 million（約32億円） to expand internationally, hire research and engineering staff, and deepen customer deployments. The company has opened an office in Houston (joining London headquarters and Bengaluru operations) to serve North American customers and is preparing to announce a partnership with a European oil major in the coming weeks. An expansion into the Middle East is also in the works.
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Applied Computing enters the energy sector at a moment when industrial AI is becoming more sophisticated but also more competitive. The core problem Adamson identifies—that facilities collect extensive sensor data but use less than 8% of it for operational decisions—reflects a longstanding challenge in industrial automation: integrating diverse data streams and knowledge domains faster than human operators can manage. The startup's insight is that this is fundamentally an AI engineering problem rather than a data or domain-expertise problem. By assembling top AI researchers and combining multiple specialized model types (time series, physics-based, and language models) rather than relying on a single large language model approach, Applied Computing positions itself as solving a technical barrier that competitors like AspenTech, AVEVA, Cognite, and Seeq have not fully cracked.
The KBR partnership is strategically significant beyond the funding itself. KBR's integration of Orbital into its INSITE 3.0 platform and its use of the model for ammonia production suggests not just validation but distribution and customer access. Adamson's emphasis on operational data—data from real working plants that cannot be replicated in simulation—points to a key competitive moat. The combination of double-digit millions in annual recurring revenue in under 18 months and partnerships with established industrial players and energy majors indicates that the market is willing to adopt the technology. However, Applied Computing must now navigate entrenched vendors and prove that its AI engineering advantage can scale across different plant types and geographies as it expands to the Middle East and deepens North American presence.
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