From the Plant to Enterprise: GenAI-Powered Energy Operations

EPAM’s first engagement focused on the plant floor, where engineers needed fast, trustworthy access to maintenance knowledge spread across fragmented documentation.

Working alongside AWS, the client first conducted a proof of feasibility, testing retrieval augmented generation (RAG) against a small set of documents to validate the approach. Building on this assessment, EPAM was brought in as the implementation partner to develop the cutting-edge RAG chatbot from the ground up by leveraging our extensive experience in AI, document digitization and energy solutions.

To begin the project, we collaborated with the organization to validate the existing architecture and delivery plan to align with its goals and ensure scalability across all power plants.  From there, we deployed EPAM DEPS (Document Extraction and Processing System), an advanced AI-powered tool that seamlessly digitizes and organizes documentation from multiple formats. This platform enables structured, searchable digital archives for the company’s plants. EPAM DEPS provides:

• Document organization into sections and subsections for easy reference
• Enhanced optical character recognition (OCR) to capture obscure or handwritten data
• A robust user interface for managing, updating and categorizing documents

Leveraging DEPS to provide digitized documentation, our team of AI engineers, cloud and DevOps engineers, QA specialists, solution architects and UI developers built the AWS-native GenAI chatbot. We used a Golden Data Set Evaluation Mechanism to train the models and ensure solution response accuracy. This means that an expert mechanic provided answers to frequently asked questions, and the answer was compared to the response generated by the AI model. This approach ensures better accuracy for the chatbot responses. 

Before launch, we worked closely with the company on change management and talent enablement initiatives to ensure adoption of the solution among the mechanics. Just 13 weeks after the project began, the digital assistant was launched in its first power plant.

The success of the maintenance assistant did more than solve a plant-level problem. It gave the client a proven GenAI architecture, established trust in cited answers and created a model that could extend into corporate research and compliance workflows.

On that foundation, EPAM expanded the assistant, originally designed for plant engineers, into an enterprise workspace, serving corporate teams. This second phase marked a strategic shift: from maintenance support to enterprise decision support, from static internal manuals to blended internal and external intelligence and from localized usage to governed self-service across teams.

The workspace combines trusted external sources with user-managed content — delivering real-time, cited answers to market, regulatory and finance questions while keeping data securely within the client’s ecosystem. It allows self-service uploads of PDF, Word and Excel files from personal and team workspaces, with access governed through Active Directory groups to ensure confidential materials don’t leave the client’s perimeter. Enterprise-grade role-based access control, encryption, audit trails and safety guardrails, including harmful content filters and out-of-scope handling, protect data integrity across all interactions. To reduce noise and hallucinations, the system respects document caps, surfacing up to six cited documents in the interface while processing roughly 15 per query.