Applied Computing, a British AI company, has entered into a partnership with Wipro Limited and Databricks to support energy operations across the Middle East, India, and Southeast Asia. The collaboration focuses on deploying AI technologies to improve operational efficiency and support energy transition efforts.
Wipro will work with Applied Computing to implement Orbital, an AI platform designed for energy environments. The aim is to integrate explainable AI into operational workflows using the Databricks platform, moving beyond pilot projects into production use.
The partnership addresses the need to modernise ageing infrastructure in the energy sector, alongside increasing decarbonisation requirements and market volatility. It combines Wipro’s experience in energy operations and digital transformation with Applied Computing’s physics-informed AI models to develop systems designed to deliver measurable and reliable outcomes.
AI is becoming more widely used in the energy sector, where there is a need for systems that incorporate both predictive capabilities and an understanding of physical processes. The use of physics-informed models integrates engineering principles and process constraints into AI systems to support operational decision-making.
Traditional approaches in the energy industry often use only a portion of available operational data. This partnership applies multi-modal foundation models that process time-series data, simulations, and engineering inputs together to provide a broader operational view.
Wipro’s experience in digital solutions and cloud migration supports the transition of AI systems from pilot stages to production environments, with an emphasis on minimising disruption. Initial deployments will focus on downstream refining and petrochemical operations due to their complexity and scale.
The collaboration uses the Databricks platform for AI deployment and data management. Its architecture supports secure and governed data handling, which is important for energy companies operating in regulated environments. Features such as centralised governance and data sharing are designed to support collaboration while maintaining control over data.
The AI systems include explainability features to allow engineers to review how outputs are generated, supporting decision-making related to safety, compliance, and performance. Initial deployments will be assessed against operational metrics, including efficiency, emissions, and financial performance.
