The global race for industrial artificial intelligence has found a new epicenter in India. British technology firm Applied Computing officially opened its new headquarters in Bangalore today. This strategic move signals a major shift in how the energy sector plans to deploy next generation AI models.
Former Shell AI chief Dan Jeavons leads this ambitious expansion.
The opening marks a pivotal moment for the energy industry. It transitions from basic digital tools to complex foundational models capable of managing live refineries. This expansion creates immediate high value roles in AI research, engineering, and energy modeling within the region.
Bangalore Emerges as the Heart of Industrial AI
India is no longer just a back office for global tech firms. It has become the primary innovation hub for deep tech and industrial artificial intelligence. Applied Computing selected Bangalore to anchor its operations due to the exceptional density of engineering talent available in the city.
The new office serves as the central command for developing “Orbital.” This flagship platform is already live inside major refining environments.
Why Bangalore?
- Talent Density: Home to the world’s largest concentration of AI engineers.
- Innovation Ecosystem: A thriving network of deep tech startups and research institutes.
- Global Capability: Proven track record of delivering critical software for Fortune 500 companies.
The company plans to scale its workforce significantly over the coming months. These are not just support roles. They are core research and development positions focused on solving the hardest problems in physics and energy.
modern bangalore office building facade with digital energy overlay
Orbital Platform Bridges Physics and Language Models
Most AI models today are good at writing text but bad at understanding the physical world. Applied Computing aims to fix this gap with Orbital. It is the first foundation model built specifically for the high stakes world of energy operations.
Orbital combines three critical elements:
- Physics: It understands gravity, pressure, and heat.
- Time Series Data: It processes sensor readings from machines in real time.
- Language: It allows engineers to ask questions in plain English.
This combination creates a “superintelligent” system. It grounds its answers in the laws of physics. This is vital for industrial sites where a wrong answer can lead to safety hazards or massive financial losses.
“For our world, the world of energy operations, that is an absolute game changer,” says Dan Jeavons. “It brings into one integrated model almost every question you could ask about the operations of a site.”
Standard large language models often hallucinate or make up facts. Orbital is designed to avoid this by cross referencing every output against physical realities. This reliability has convinced leading operators to deploy it in some of the most complex industrial landscapes on earth.
Shell Veteran Dan Jeavons Takes the Helm
The expansion into India is spearheaded by Dan Jeavons. He is a recognized heavyweight in the industrial sector. Jeavons previously spent two decades at Shell. He led their global digital innovation and computational science programs.
He relocated from London to Bangalore several years ago and chose to stay.
Jeavons describes his decision to join Applied Computing as his personal “ChatGPT moment.” He saw the potential of Orbital to solve problems that had frustrated the industry for years.
Dan Jeavons’ Track Record:
- Former Role: VP of Computational Science and Digital Innovation at Shell.
- Team Size: Led 350+ researchers globally.
- Scope: Managed AI for wind turbines, seismic processing, and manufacturing.
He met the company founders during his tenure at Shell. He recognized the unique approach Sam Tukra and Callum Adamson were taking. They were not just wrapping a skin around existing models. They were building something entirely new from the ground up.
A New Era for Energy Operations
The deployment of foundation models like Orbital represents a step change for the energy sector. Companies are under immense pressure to optimize efficiency and reduce emissions. AI is the only tool powerful enough to manage these conflicting variables at scale.
Real world applications are already visible.
Operators use these tools to predict equipment failures before they happen. They optimize production flows to save energy. They can query complex data sets to make faster safety decisions.
| Traditional AI | Applied Computing’s Orbital |
|---|---|
| Requires manual labeling of data | Learns from raw unstructured data |
| Specific to one single task | General purpose foundation model |
| Often ignores laws of physics | Grounded in physics and engineering |
| Hard for non experts to use | Natural language interface for everyone |
This shift democratizes access to high level data analysis. An engineer on the ground can now use simple language to get answers that previously required a team of data scientists to compute.
The Bangalore office is now open for business. It stands as a testament to the growing importance of India in shaping the future of global energy infrastructure. The team is actively recruiting top tier talent to join this mission.
This is just the beginning of the industrial AI revolution.
