An energy company faced significant challenges extracting tag numbers associated with thermal heater symbols across thousands of CAD schematics. Engineers and technical teams spent countless hours manually locating schematic symbols and capturing corresponding tag information, a process that was both time-consuming and susceptible to human error. As the volume and complexity of engineering drawings continued to grow, maintaining accurate asset records, inventory tracking, and compliance documentation became increasingly difficult.
To streamline the process, the company implemented the Cognitive Data Extractor (rannsCDE), an AI-driven solution designed to automate the identification of thermal heater symbols and extraction of associated tag numbers from CAD schematics. Leveraging advanced image recognition and document intelligence models, rannsCDE accurately detected heater annotations within high-resolution engineering drawings and extracted relevant tag information from surrounding technical details. By eliminating manual review and data entry, the solution significantly improved extraction speed, consistency, and accuracy while enabling large-scale processing of engineering documents.
With rannsCDE in place, the company successfully automated the indexing of more than 25,000 CAD schematics every month while achieving over 95% AI detection accuracy. Manual effort was reduced by more than 70%, allowing engineering teams to focus on higher-value tasks rather than repetitive document review. The solution enhanced engineering asset management, improved inventory visibility, and strengthened compliance documentation processes, delivering measurable efficiency gains across operations.
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