GenAI Accelerator for Energy Industry

For the energy industry, generative AI-based systems will initially serve as chat or natural-language interfaces that complement existing ML- and AI-based systems. But the potential for generative AI across the energy value chain is enormous.

Many organizations are faced with the following questions including: 1 Where do we start 2 How do we address concerns around data quality, privacy, and security 3 What if we have limited expertise in analytics and machine learning 4 How do we address legacy data estates and on-premise systems 5 What are real-world industry use cases with ROI

Our team has completed data & analytics projects for Canadian Energy, Transportation, Agriculture, Mining, Real Estate & Financial Services organizations. We bring to our joint customers the flexibility and depth to adapt to and work with teams from the brightest and most technical developers to non-technical business users. While we specialize in GenAI, we also bring a deep understanding of broader challenges around data and applications. We believe it is critical to align analytics projects to both your data and corporate strategy and help clients with gaining buy-in, addressing change management, and integrating legacy systems and platform decisions to modernize your data estate and capabilities while balancing critical foundations such as governance, security and user experience.

Our GenAI QuickStart for Energy offering allows for organizations to work with a partner that has deep Energy experience, and come with use cases, systems and process knowledge, and real-world use cases. We help navigate the best use case for your organization and provide a detailed roadmap to production so that your POC isn't "throw-away".

WEEK 1: KICK-OFF • Use Case Ideation/Validation including feasibility, ROI, TCO • Data Capture & Ingestion

WEEKS 2 – 3: POC CREATION & IMPLEMENTATION ROADMAP DEVELOPMENT

• AWS Environment Set-up • Knowledge Base and Model Creation • Creation of the GenAI Model and associated knowledge bases • Establishment of a web portal/access for testing and evaluation • Iterations on prompt engineering/parameter tuning and output templates • Evaluation of overall performance against current practices and procedures • Development of a functional POC environment on AWS including walk-through & knowledge transfer • POC Conclusion and Read-Out

DELIVERABLES • "Roadmap to Production" including AWS service costs, build estimates, and costs to maintain solution