Engineers in an energy infrastructure company were overwhelmed with the task of manually reviewing the pipeline integrity reports that were mandatory for regulatory compliance, particularly with PHMSA (Pipeline and Hazardous Materials Safety Administration). The process of extracting data from unstructured reports, identifying bare pipe and B-sleeve locations, and correlating In-Line Inspection (ILI) from dense tables was slow, repetitive, and prone to errors. All of which not only delayed critical assessments but also came with the inability to maintain accurate audit trails and timely defect detection.
To address this, the company partnered with Rannsolve to deploy its AI-powered Cognitive Data Extractor (CDE), designed to automate the extraction of structured data from unstructured pipeline integrity reports. CDE intelligently parsed documents to identify inspection details, map bare pipe and B-sleeve locations, and align correlated ILI data from embedded tables. This significantly reduced the time it was manually handled and allowed engineers to make faster, more informed decisions regarding pipeline maintenance and risk mitigation.
The company achieved an impressive feat with over 95% AI detection accuracy and a 70% reduction in manual effort. Compliance readiness and audit traceability were improved remarkably. The AI-driven CDE not only improved defect identification but also brought its pipeline integrity report data extraction.
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