AI-Powered Precision helps slash Rail Engineering Costs by 96% with Multi-Agent Automation
United States
Challenge
A top-tier European rail corporation managing 25,000+ km of track, facing inefficiencies in digitizing legacy rail signaling diagrams.
Excessive Labor: 100-150 engineer hours per job spent manually converting scanned diagrams.
Error-Prone Processes: Manual symbol recreation led to accuracy issues and metadata loss.
Unsustainable Costs: Six-figure annual labor expenses with no scalability.
Stalled Innovation: Engineers bogged down in tedious tracing instead of strategic design.
Solution
Akraya's Team introduced a two‑agent LangGraph workflow which ensured maximum output with least manual intervention.
Vision Extractor Agent: PaddleOCR & Detectron2 for multilingual text/table extraction and 50+ rail symbol detection (93% F1 accuracy). Converted PDF scans into JSON blueprints with metadata tags.
CAD Builder Agent: PyAutoGUI and custom macros to auto-place symbols, snap wires, and populate attributes in AutoCAD. LangGraph orchestration for scalable, fault-tolerant workflows.
Results
Operational: 96% faster conversions (120h → <4h per job); 93% symbol accuracy; zero metadata loss.
Financial: Six-figure annual savings; ROI in <3 months; engineers shifted 90% time to innovation.
Business: Scalable framework for 10x project volume; automated compliance with evolving rail standards.
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