An energy infrastructure company faced significant challenges managing the large volume of pipeline integrity reports required for regulatory compliance, particularly with the Pipeline and Hazardous Materials Safety Administration (PHMSA). Engineers were spending considerable time manually reviewing unstructured reports, locating bare pipe and B-sleeve defects, and correlating In-Line Inspection (ILI) data embedded within complex tables. This labor-intensive process increased the risk of errors, delayed critical integrity assessments, and made it difficult to maintain comprehensive audit trails and timely defect identification.
To streamline pipeline integrity management, the company partnered with Infognana to implement its Cognitive Data Extractor (rannsCDE), an AI-driven solution designed to convert unstructured reports into actionable structured data. Using advanced document intelligence, rannsCDE automatically identified inspection details, extracted bare pipe and B-sleeve locations, and correlated ILI data from embedded tables. By automating these processes, engineers gained faster access to critical information, enabling more efficient maintenance planning, risk assessment, and compliance reporting.
The implementation of rannsCDE delivered more than 95% AI detection accuracy while reducing manual effort by over 70%. The company significantly improved compliance readiness, strengthened audit traceability, and accelerated defect identification across its pipeline network. By automating the extraction and analysis of pipeline integrity data, the organization enhanced operational efficiency and empowered engineering teams to focus on higher-value decision-making and risk mitigation activities.
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