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Data Science for the Energy Industry


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We develop AI strategies and models for clients in the Energy Industry

We bring subject matter experts, business consultants, data scientists and software engineers together to solve precisely defined operational challenges. Our outputs are robust and deployable AI solutions which are based on data science best practice and ready for full deployment within client management or operational systems.

The conditions for success in deploying AI can only be created through a complete understanding of the specific challenges to be solved, a detailed analysis of the available data and knowing the limitations of each AI approach.

No two clients are the same. As a lab we develop AI solutions which are designed to solve your specific challenges

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Exactly how AI can create value can be difficult to conceptualise. Our use cases for the energy industry contain examples of different challenges and potential AI approaches that have proven to work.

AI use case


From AI strategy to implementation, we help energy industry clients transition to being data driven organisations.

Image by Jared Arango

The journey to becoming a data-centric company should always feature proof-of-concept projects. These projects have strategic alignment, a strong business case and a clear value proposition. Developing a proof of concept is a step-by-step process which has data and client involvement at its core.


of Concept

AI proof of concept
  • Identify quantifiable technical, engineering or operational challenges which have sufficient data for AI

  • Explore the data and existing data management process to understand which AI approaches are most likely to deliver value

  • Prepare the data for modelling

  • Develop and test different models and workflows using project management methodologies

  • Evaluate and refine towards the best solution

AI strategy

Every successful AI implementation starts with a clear strategy. Our AI strategy development services focus on:

  • Determining which specific technical, engineering or operational challenges should be prioritised for AI projects

  • In a portfolio context, identifying the most valuable AI use cases and aligning them with an overall digital strategy

  • Establishing governance frameworks with clearly defined roles, responsibilities and communication structures to ensure AI projects can succeed and grow

  • Defining the critical pathway to AI implementation while addressing cross-cutting issues such as infrastructure, competencies and organisation to streamline business transition towards a data-centric company





We are proud to be different.

We start from problem definition 

Our projects never start with looking into large volumes of data, hoping to find golden nuggets. We  start by precisely defining the technical challenges to be solved.

We focus on model development

We believe in creating the best solution for a challenge, not in force-fitting existing solutions. Sometimes this requires starting from scratch, but more frequently requires different existing models to be combined.

We are a multi-disciplinary team

Our team comprises data scientists, computer scientists, engineers and business consultants. This balanced team allows us to understand the business context and how our projects can best deliver value.

Ready to take the next step?

Start working with us today and accelerate your AI journey.

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