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Applied Computing wants to give oil and gas operators an AI model for the entire plant
Applied Computing, a London-based startup founded in 2023, has secured $20 million in Series A funding to develop artificial intelligence models specifically for the oil and gas industry. The company created Orbital, an AI system combining time-series analysis, physics-based modeling, and language processing to help energy operators extract value from their sensor networks. Currently, facilities use less than 8% of the data they collect because integrating thousands of sensor readings with engineering documentation and chemical principles occurs too slowly to inform real-time decisions.
Orbital accelerates this process by condensing investigations that previously required weeks into minutes, enabling operators to identify anomalies, determine root causes, and model potential impacts across interconnected systems. The startup has reached double-digit millions in annual recurring revenue within 18 months and operates across several large, publicly-traded energy companies, with partnerships including Wipro and KBR. Despite facing entrenched competitors like AspenTech and AVEVA, Applied Computing argues its strength lies in assembling top AI researchers capable of building genuinely competitive models—a moat fundamentally different from merely accessing industrial data or operational expertise.
In short
- London startup Applied Computing raised $20M for Orbital, an AI system that synthesizes sensor data with physics models for oil and gas facilities
- Claims to compress weeks-long operational investigations into minutes; already generating millions in annual revenue from major energy companies
- Differentiates from rivals like AspenTech through AI talent acquisition rather than proprietary data or domain expertise
Steelman: both sides, in good faith
The strongest fair case each way — we don't pick a winner.
The case for
Applied Computing's technology addresses a genuine operational inefficiency that affects safety, cost management, and environmental compliance across existing energy infrastructure. Since oil and gas will remain part of the global energy system for decades, improving operational performance through better data utilisation represents a pragmatic contribution to more efficient, safer production and reduced waste—goals that benefit consumers, investors, and the companies' environmental obligations whilst the industry remains operational.
The case against
By optimising fossil fuel extraction, Applied Computing extends the economic viability and competitiveness of an industry humanity must transition away from, not improve. This diverts capital, talent, and attention from accelerating renewable energy development and energy system transformation; from a climate perspective, the priority should be reducing investment and efficiency gains in hydrocarbon infrastructure, not enhancing them, to hasten the essential shift to sustainable alternatives.
Explained simply
Oil and gas plants collect enormous amounts of data from thousands of sensors, but struggle to make sense of it all quickly enough to act on. Most of this data goes unused because turning sensor readings into useful decisions takes too long. This leaves operators unable to spot problems, understand what causes them, or predict what might happen across their entire facility.
Applied Computing is a London startup that has developed an AI system called Orbital to tackle this problem. The technology uses artificial intelligence to speed up the process of analysing sensor data and connecting it to engineering documentation. Instead of investigations taking weeks, Orbital can complete them in minutes.
The company has attracted $20 million in funding and already works with several large energy companies, which suggests the technology is proving valuable. If AI systems can help oil and gas operators extract more value from their existing data and make faster decisions, it could affect efficiency and operations across the energy sector. Applied Computing faces competition from established industrial software companies, but argues its advantage lies in the strength of its AI researchers rather than simply having access to more data.
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