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Transforming Technical Document Digitization With AI
A leading liquefied natural gas (LNG) energy company faced an unsustainable challenge: manually transcribing thousands of complex Piping & Instrumentation Diagrams at 1-2 hours per document. We developed an AI-powered tiling architecture that achieved 70%+ extraction accuracy in just three weeks, reducing processing costs from hundreds of hours of engineering time to $1.50 per document and laying the foundation for automating a library of 2,000+ documents.
Industry
Energy
Teams & Services
Tech & Tools
AWS Bedrock (Claude 3.5 Sonnet), Lambda, S3, DynamoDB, Terraform, Python
Key Data Points
The Vision
What began as a proof of concept is now a blueprint for transforming how energy and industrial organizations manage their most critical technical assets. By combining AI-powered extraction with a scalable tiling architecture, engineers can move away from hours of manual transcription and toward instant, automated document processing at a fraction of the cost.
The vision extends far beyond a single document library. With the foundation in place, the next step is a system where thousands of documents can be queried in natural language, changes detected across revisions in real time, and preventive maintenance triggered automatically, with applications reaching across construction, manufacturing, utilities, and beyond.
The Goal
The goal was to prove that AI could reliably digitize complex Piping & Instrumentation Diagrams at scale, starting with a focused three-week sprint to extract equipment tags from 10 of the most technically demanding documents in the library. With a target accuracy of 85% or higher and zero tolerance for false positives due to strict regulatory compliance requirements, the project needed to demonstrate not just technical feasibility, but real commercial viability as a foundation for automating a library of 2,000+ documents.
The Challenge
Digitizing thousands of physical Piping & Instrumentation Diagrams, some dating back to the 1900s, presented a challenge that went far beyond simple document scanning. Each diagram contained small, highly detailed equipment tags requiring high-resolution processing, complex technical symbology, and specialized notation that standard OCR tools couldn't reliably interpret.
With documents sourced from multiple vendors and a strict zero hallucination tolerance driven by regulatory compliance requirements, the margin for error was essentially nonexistent. At 1-2 hours of manual engineering time per document, processing a library of 2,000+ diagrams was simply unsustainable, making a scalable, accurate, and cost-effective automated solution not just desirable, but critical.
The Solution
We developed a tiling-based architecture that breaks each page into roughly 40 high-resolution tiles, processed independently through Amazon Bedrock, then aggregated into a clean, validated output. This approach solved the image quality issues that had made equipment tags unreadable, pushing extraction accuracy from near zero to 70%+ in just five days.
Paired with advanced prompt engineering that constrained the AI to search only for relevant tags and return nothing when uncertain, false positives were effectively eliminated. The result was a fast, reproducible pipeline built on fully Terraform-managed infrastructure, capable of processing each document in 4-6 minutes and validated across multiple document sources and formats.
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