Enterprise AI Search and Chatbot: Energy Sector

The Challenge

A major energy company had decades of critical technical knowledge locked inside 300+ GB of unstructured documents: PDFs, Word files, Excel spreadsheets, PowerPoint presentations, and SAP records. Engineers spent hours manually searching through 1,000–6,000 page technical manuals to find specific procedures, compliance requirements, and historical data.

The Solution

• Custom Document Parsers: Off-the-shelf parsers failed on the company's complex document formats. Built custom parsers using RAGFlow Deepdoc combined with in-house parsing logic to handle tables, diagrams, headers, and domain-specific formatting across 5+ file types.
• Optimized RAG Pipeline: Designed a retrieval pipeline specifically tuned for long technical documents (1K–6K pages), including domain-aware chunking strategies, metadata-enriched embeddings, and multi-stage retrieval with re-ranking.
• Source Verification and Downloads: Every answer includes source citations with page numbers and direct document download links, critical for compliance-driven industries where engineers must verify information against the original source.
• Production Backend: Built on FastAPI with async processing, connection pooling, and caching to handle concurrent queries from multiple engineering teams.

Key Results

• Reduced research time from hours to minutes; engineers now get verified answers in seconds.
• Processed 300+ GB of unstructured data across 5+ document formats.
• Custom parsers achieved reliable extraction from complex technical documents that broke commercial parsing tools.
• System adopted across multiple engineering teams, becoming part of daily workflows.
• Improved compliance by making it easier to locate and verify regulatory and procedural information.