MOI digitizes survey reports, scans, cross-page tables, and drawings while preserving layout semantics and source evidence, making historical archives searchable and reviewable.
SGIDI (Shanghai Geotechnical Investigations & Design Institute Group) is one of China's leading geotechnical engineering and urban infrastructure consultancies, providing geological surveys, foundation design, underground engineering and construction supervision. Over decades of project delivery it has accumulated a substantial engineering archive.
Engineering documents-geological survey reports, soil stratigraphy tables, foundation drawings, structural calculations-contain critical project data, but this information is locked inside unstructured formats: scanned PDFs, CAD files, handwritten field notes, and complex multi-column tables with domain-specific notation. Manually extracting key parameters (soil layer properties, bearing capacities, design loads), classifying document types, and tagging them for retrieval is extremely labor-intensive and error-prone. Engineers spend significant time searching archives rather than doing engineering work, and institutional knowledge embedded in historical projects remains largely inaccessible.
SGIDI deployed an AI Drawing Structuring & Classification System on OmniFabric, automating key information extraction from engineering documents, intelligent classification by document type, and structured tagging for fast retrieval and downstream analysis.
Complex engineering tables-soil stratigraphy, physical-mechanical parameters, bearing capacity data-are automatically extracted and structured, making decades of project data instantly queryable.
Documents are intelligently classified by type (survey report, design drawing, supervision record) and tagged with domain metadata (project ID, region, soil conditions), eliminating manual filing effort.
Cross-project search by soil type, design parameter, or geological condition lets engineers instantly locate relevant historical precedents-turning the archive from a storage burden into a knowledge asset.