The energy industry generates extraordinary volumes of rich, complex data. Yet much of it remains unused. Orbital, Applied Computing’s purpose-built AI foundation model, bridges that gap, bringing together physics, time-series data, and artificial intelligence to help energy leaders move beyond siloed optimisations and into true system-wide intelligence. 

Executive Summary 

The energy industry generates more data than almost any other sector, yet over 90% of it remains untapped. Foundation models purpose-built for the industrial world are set to change that. At Applied Computing, we have developed Orbital, a model that merges physics, time-series intelligence, and advanced AI to deliver true contextual understanding, explainability, and near-zero hallucinations. These are features essential for high-stakes environments like energy, where even the smallest error can have major real-world consequences. By unifying fragmented systems and enabling real-time, data-driven optimisation, Orbital doesn’t just enhance how energy operates today; it helps define the energy intelligence of tomorrow. 

The Untapped Power of Industrial Data

For decades, the energy sector has been defined by complexity. Millions of sensors stream torrents of operational data, creating one of the most data-rich environments on the planet. Yet less than 10% of this data informs decisions on performance. The remaining 92%, untapped, unstructured, and siloed, has led to what many describe as industrial gridlock. The result is a fragmented landscape of localised optimisations that individually address a specific problem, but collectively fail to achieve true system-wide intelligence. 

Recent advances in AI have demonstrated what’s possible when machines learn context at scale. General-purpose models, built for text or images, fall short in high-stakes industrial environments. In energy, where every decision carries real-world consequences, accuracy and explainability matter far more than probabilistic regeneration. 

This realisation has driven Applied Computing to take a novel approach. Rather than retrofitting consumer AI for industrial use, the team has created Orbital, a foundation model designed from the ground up for energy. Our approach combines contextual understanding, scientific credibility, and zero tolerance for hallucinations, features that are mission critical in complex environments like the energy value chain.

From Silos to Integrated Systems

The data challenges facing energy companies are not new. For decades, operators have relied on isolated systems to manage supply chains, production optimisation, and maintenance. Each excels at solving a specific use case, but together create a disconnected intelligence mesh that caps the true frontier of optimisation. In today’s AI era, that ceiling becomes anachronistic. System-wide intelligence, where every part of the value chain speaks to each other in a symbiotic language, is now possible. Yet most of the industry is still optimising silos. 

A foundation model built for energy can serve as a unifying intelligence layer, creating a system that understands multiple data types and relationships simultaneously. Instead of developing hundreds of point solutions, companies can begin to address entire networks of interrelated challenges at once. 

This systemic shift has become viable thanks to advances in cloud computing and modern data architectures. Today, platforms like Databricks enable the processing of trillions of time-series data points in parallel, transforming what used to be computationally impossible into an everyday capability. 

Addressing Hallucinations by Learning the Physics of Energy

Energy data looks nothing like the bytes that power general LLMs. It is dense, continuous, and deeply contextual. Think minute-interval streams of pressure readings, vibration signals, and control-system parameters. What’s more astounding is around 8% of this time-series data currently informs operational decisions. 

A model built for energy must understand this energy language. This means grounding predictions in the physics of energy that underpin decades of engineering experience. By embedding science directly into the foundation model, Applied Computing has created a system that doesn’t just process data; it comprehends the operating environment itself. 

This contextual fidelity also underpins one of Orbital’s non-negotiable design principles: preventing hallucinations. In consumer applications, occasional inaccuracy can be tolerated. In industrial operations, a tiny lapse is fatal. A model that misreads a sensor pattern or generates a plausible-sounding but incorrect recommendation risks more than confusion. It risks costly downtime or, worse, catastrophic implosions that endanger human lives.

Orbital combines contextual understanding, scientific credibility, and zero tolerance for hallucinations, features that prove mission critical in complex environments like the energy value chain.

Built for Trust and Security

Trust is central to whether AI is feasible in energy. Recognising that energy data is among the most sensitive information in any organisation, our team has embedded cybersecurity into the core design of Orbital from day one. The model can operate at the edge or in the cloud, and can be fully contained within an organisation’s environment to ensure complete control over its data. 

Partitioned training techniques guarantee that no customer’s data can be reverse-engineered or inadvertently exposed to another entity. This layered security architecture, combined with advanced encryption and partnerships with leading technology providers, creates the foundation for a trustworthy AI ecosystem.

Recognising that energy data is among the most sensitive information in any organisation, Applied Computing has embedded cybersecurity into the core design of Orbital from day one. The model can operate at the edge or in the cloud, and can be fully contained within an organisation's environment to ensure complete control over its data.

The Human Dimension

The adoption of AI at scale in energy depends on engineers, operators and analysts willing to collaborate with synthetic intelligence. This collaboration of human and artificial intelligence also represents a core tenet of change management sweeping across the energy sector today. It requires professionals to view AI not as a replacement, but as an extension of their expertise. The message is clear: “AI won’t take your job, but the engineer who learns how to use AI will.”

AI in Energy Transformation

The energy industry stands at an inflection point. After decades of fragmented digital progress, a new model for intelligence is emerging, one that brings together the full breadth of data, experience, and science that underpins the sector. The opportunity is enormous: to unlock the 92% of data still untapped, to eliminate industrial gridlock, and to empower human expertise with tools built for trust. 

This inflection isn’t just about technology; it’s about industry-wide transformation that is redefining the frontier of performance and optimisation in energy.  

The convergence of domain expertise and cutting-edge AI marks a new era of intelligence, one that evolves the energy sector from siloed optimisations to system-wide intelligence. Led by models like Orbital, this evolution is defining how energy systems will operate in the AI age.