Prabowo Widodo
London-based startup Applied Computing, a pioneering force in the development of foundational AI models for the global oil, gas, and petrochemical industries, has successfully closed a $20 million Series A funding round. The significant investment was spearheaded by engineering titan KBR, with notable participation from Databricks Ventures, underscoring the growing confidence in AI’s transformative potential within heavy industries. This capital injection is poised to accelerate the company’s ambitious international expansion, fuel critical research and engineering advancements, and facilitate broader deployments with key energy sector clients globally.
A New Era for Industrial Data Intelligence
Founded in 2023, Applied Computing has rapidly positioned itself at the forefront of industrial AI, addressing a critical pain point within complex operational environments. The target sectors—oil, gas, refining, and petrochemical systems—are characterized by an overwhelming volume of data generated by thousands of sensors. These sensors meticulously monitor a vast array of parameters, from fundamental measurements like temperature and pressure to more intricate metrics such as velocity and viscosity. Despite this rich data landscape, the industry has long grappled with significant data fragmentation, leading to suboptimal operational decisions.
Callum Adamson, co-founder and CEO of Applied Computing, highlights the stark reality of this challenge: facilities typically utilize less than 8% of the available data when making crucial operating decisions. This inefficiency stems not from a lack of data collection, but from the profound difficulty in seamlessly integrating disparate data sources. "Operators already collect much of this information," Adamson explained in an interview, "but they struggle to combine the sensor readings, engineering documentation, and physics and chemistry quickly enough to analyze and make predictions." He further emphasized the core problem: "It’s getting those three data sources to talk to each other in real time. That’s the real key." This inability to synthesize vast, varied datasets in a timely manner results in delayed insights, reactive problem-solving, and missed opportunities for optimization.
Orbital: A Hybrid AI Model for Complex Systems
Applied Computing’s flagship product, Orbital, represents a paradigm shift in industrial AI. Unlike large language models (LLMs) that focus on predicting the next word in a sequence, Orbital is engineered to predict the state of an entire facility. This advanced capability is achieved through a sophisticated hybrid model architecture that integrates three distinct yet interconnected components: a time series model, a physics-based model, and a language model.
The time series model excels at analyzing continuous streams of sensor data, identifying patterns and anomalies over time. The physics-based model incorporates fundamental engineering principles and chemical reactions relevant to the specific industrial processes, ensuring that predictions adhere to the laws of nature and the operational constraints of the equipment. Finally, the language model processes engineering documentation, operational manuals, and even operator notes, providing contextual understanding of the facility’s design, maintenance history, and human interactions. By combining these elements, Orbital can analyze sensor readings, interpret them within the bounds of physics and chemistry, and recognize equipment limitations and operator activities.
A particularly powerful feature of Orbital is its ability to run simulations. Technicians can model how a change in one part of a facility might ripple through and affect the rest of its complex operations. This proactive simulation capability is invaluable for testing hypothetical scenarios, optimizing processes, and predicting potential issues before they arise, moving operators from reactive problem-solving to predictive management.
The Promise of Unprecedented Speed and Efficiency
The core value proposition of Applied Computing and its Orbital platform is speed. The company asserts that Orbital can identify anomalies, investigate their root causes, and simulate the potential impacts of proposed solutions across the facility—all within minutes. Adamson claims that the product can compress investigations that traditionally consumed days or even weeks into mere seconds. This dramatic reduction in diagnostic and decision-making time has profound implications for operational efficiency, enabling operators to swiftly reduce energy consumption, maintain optimal output levels, and mitigate costly downtime.
In a sector where a single day of unplanned downtime can cost millions of dollars, the ability to rapidly identify and resolve issues represents an immense competitive advantage. For example, a major refinery can incur losses ranging from $5 million to $20 million per day during an unscheduled shutdown. By enabling faster, more informed decision-making, Orbital directly contributes to improved uptime, enhanced safety, and greater profitability for energy companies.
Rapid Market Traction and Strategic Alliances
The promise of such rapid operational insights has resonated strongly within the industry. Applied Computing has demonstrated remarkable market traction, transitioning from stealth mode to achieving double-digit millions in annual recurring revenue (ARR) in less than 18 months. This impressive growth trajectory is a testament to the urgent demand for advanced AI solutions in the energy sector and Orbital’s proven efficacy.
Adamson revealed that Orbital is currently deployed and in active use at several "large, publicly listed" upstream oil and gas, downstream refining, and petrochemical companies. While specific customer numbers were not disclosed, the caliber of these early adopters underscores the platform’s robust capabilities and the trust it has garnered.
Strategic partnerships have been instrumental in Applied Computing’s rapid ascent. The company has forged alliances with key industry players, including Indian energy giant Wipro, which serves as a significant partner in broader digital transformation initiatives. Crucially, engineering leader KBR has not only invested in Applied Computing but has also integrated Orbital into its INSITE 3.0 digital platform, which is designed for energy projects. KBR is actively leveraging Orbital for critical applications, including ammonia production, showcasing a deep operational commitment to the technology. Looking ahead, Applied Computing is also collaborating with a "major U.S. upstream operator" and plans to announce a partnership with a prominent European oil major in the coming weeks, signaling continued expansion into core energy markets.
Navigating a Competitive Landscape: Applied Computing’s Unique Moat
The industrial software market is well-established, featuring entrenched suppliers and a growing number of specialized AI startups. Companies like AspenTech offer sophisticated simulation and AI-powered modeling software across upstream, refining, and chemical operations. AVEVA provides physics-based process simulation, optimization, and "what-if" modeling tools for industrial plants. Furthermore, firms such as Cognite and Seeq focus on the data layer, assisting facilities in analyzing vast industrial datasets and applying AI to optimize workflows.
Despite this competitive environment, Adamson asserts that Applied Computing possesses a distinct competitive advantage, or "moat," that sets it apart. He argues that the company’s core strength lies not merely in access to industrial data or process knowledge, but in its ability to assemble and retain top-tier AI researchers capable of building a model as sophisticated and effective as Orbital. "It’s an AI problem. It’s not a data problem, and it’s not an energy problem," Adamson stated, challenging conventional wisdom. He provocatively questioned, "If you’re a tier-one AI researcher, where are you going to work? … I don’t think Shell’s on that list." This perspective highlights Applied Computing’s commitment to pure AI innovation as its primary differentiator.
Furthermore, the unique operational data Orbital acquires through its deployments provides another formidable advantage. Operational data from real-world refineries and other energy facilities is typically proprietary and not publicly available. Adamson notes that simulated data, while useful, often fails to fully reproduce the intricate complexities and emergent behaviors observed within a live, working plant. The continuous flow of proprietary, real-world operational data into Orbital’s models creates a powerful feedback loop, allowing the AI to learn, refine, and improve its predictive capabilities in ways that simulated data cannot replicate. The partnership with KBR further strengthens this moat, granting Applied Computing invaluable access to additional operational data, deep industry expertise, and introductions to a broader network of potential customers.
Broader Implications: AI’s Role in Energy Transition and Operational Excellence
The investment in Applied Computing reflects a broader trend of digital transformation sweeping across the global energy sector. Facing increasing pressures related to operational efficiency, safety, environmental compliance, and the ongoing energy transition, companies are actively seeking advanced technological solutions. The global digital oilfield market, for instance, was valued at approximately $23.4 billion in 2022 and is projected to grow significantly, driven by the imperative to optimize production, reduce costs, and enhance safety through data-driven insights.
AI, in particular, is emerging as a critical enabler for achieving these objectives. By harnessing the power of AI to analyze vast datasets, energy companies can move beyond reactive maintenance to predictive maintenance, anticipate equipment failures, optimize energy consumption, and reduce greenhouse gas emissions. Orbital’s capability to provide real-time insights and predictive simulations directly contributes to these goals, allowing operators to fine-tune processes for maximum efficiency and minimal environmental impact. This aligns with the industry’s broader commitment to ESG (Environmental, Social, and Governance) principles, demonstrating how advanced technology can drive both economic performance and sustainability.
The participation of Databricks Ventures also signals the growing importance of robust data and AI platforms in supporting these foundational models. Databricks, known for its data lakehouse platform that unifies data, analytics, and AI, likely sees Applied Computing as a strategic investment that validates the power of their ecosystem in handling complex industrial AI challenges. This collaboration could pave the way for further advancements in how industrial data is managed and leveraged for AI applications.
Global Expansion and Future Outlook
With the $20 million Series A funding, Applied Computing is well-capitalized to execute its ambitious growth strategy. A core component of this strategy is international expansion. The company recently announced the opening of a new office in Houston, Texas, a strategic move that places it closer to two existing North American customers and the heart of the U.S. oil and gas industry. This new base complements its existing headquarters in London and its operational hub in Bengaluru, India, creating a global footprint that spans key energy markets. Further expansion into the Middle East, another critical region for oil and gas production, is also in the pipeline.
The company plans to significantly invest in hiring for research and engineering roles, further bolstering its capacity for innovation and model development. This commitment to attracting top AI talent is crucial for maintaining its competitive edge and continuously evolving Orbital’s capabilities. As the energy sector continues its digital evolution, Applied Computing is positioned to play a pivotal role, offering an AI-driven solution that promises to unlock unprecedented levels of efficiency, safety, and sustainability across the oil, gas, and petrochemical value chain. The successful funding round and rapid market adoption suggest a bright future for this London-based startup as it seeks to redefine industrial operations through foundational AI.
