Asset Tagging Extraction

AI driven iWorkflow enables efficient asset tagging extraction and identifies gaps

Carbon neutral

Up to 10x productivity gains

Up to 70% cost saving

SLB is undergoing a digital transformation project that involves tagging 15,000 pieces of equipment in 5 days, achieving 50% cost savings and 80% time savings. This project covers 2 million pages and encompasses all engineering disciplines and document types.


  1. Very often there are significant inconsistencies or missing information in asset tagging and identification due to historical engineering, vendor management, or business consolidation reasons.
  2. Given the large number of documents, manually identifying all tagging related asset information in such a large dataset becomes almost impossible. This becomes a bottleneck for digital transformation.
  3. Operation companies are moving into digital asset management system, and require more detailed asset identification and linkage with documentation.


iWorkflow Digitalization integrates iDrawings and iDocuments Ai solution, efficiently identifies asset tagging on all types of documents, making it possible to extract data from a large data set, and also helps customers to identify inconsistencies and gaps in tagging assets.


A global operations company needed to update its asset system by identifying assets from operation documents and drawings. IPS iWorkflow Digitalization helps efficiently identify asset information, tags, and gaps. Using IPS’s iWorkflow AI, they extracted over 15,000 tagged items in 5 days, identifying many gaps. This process, which would have taken 2 man-months and cost ten times more manually, is estimated to save $1M and 2 years for all assets.