From Weeks to Minutes: Automating Tag-to-Document Registers for EPC Projects

The Problem

Document control teams face a monotonous but critical task: manually extracting equipment tags from hundreds of engineering documents and creating comprehensive Tag-to-Document Registers. This isn't just data entry—it's forensic work across multiple document types and formats.

For a typical petrochemical facility, this means processing 12,000+ equipment tags across 400+ P&IDs, hundreds of GA drawings and flow diagrams, and multiple vendor documents. These documents come in various formats: PDF, DWG, and scanned images, each presenting unique challenges.

• Time-Intensive Process. A single P&ID takes 30-45 minutes to process manually. Across 400 drawings, that's 3-4 weeks of dedicated work for a single person. For projects with aggressive schedules, this becomes a critical path bottleneck.
• Error-Prone at Scale. Human fatigue is inevitable when processing thousands of tags. Even experienced document controllers make mistakes when working with repetitive data extraction over extended periods. These errors compound and propagate through downstream processes.
• Format Inconsistencies. Engineering documents are created by different vendors, disciplines, and contractors, each with their own standards. Tag placement varies, naming conventions differ across drawing types, and legacy scanned drawings often have poor image quality.

The traditional approach of throwing more people at the problem doesn't scale. Additional document controllers need training on project-specific conventions, create coordination overhead, and increase the likelihood of inconsistencies in approach.

What Plinth Delivered

We built a system that parses engineering documents regardless of format, extracts equipment tags with their attributes, and automatically maps them to source documents. The system doesn't require documents to be reformatted or standardized—it works with the messy reality of real-world engineering documentation.

The system processes P&IDs, GA drawings, flow diagrams, and vendor documents in PDF, DWG, or image format. It handles inconsistent tag placement, varying naming conventions across drawing types, different vendor standards, and even poor-quality scanned legacy drawings from older projects.

• Multi-Format Document Parsing. Native CAD file processing, PDF text extraction, and advanced OCR for scanned documents. The system automatically selects the optimal extraction method based on document characteristics.
• Intelligent Tag Recognition. Machine learning models trained on engineering documentation patterns identify equipment tags even when naming conventions vary or documents use non-standard formats. The system learns from corrections to improve accuracy over time.
• Automated Cross-Referencing. Automatically maps each tag to all source documents where it appears, creating bidirectional traceability. Identifies missing references and flags potential documentation gaps before they become commissioning issues.

Results

The system achieved 98% accuracy on its first deployment, with the remaining 2% requiring manual review for edge cases like unusual tag formats or severely degraded source documents. These exceptions are clearly flagged for human review, ensuring quality while eliminating the bulk of manual work.

Time to complete the Tag-to-Document Register dropped from 3-4 weeks to just hours of processing time plus a day of exception review. This freed the document control team to focus on higher-value activities like coordinating with engineering disciplines and preparing for commissioning handover.

Why This Matters

Tag-to-Document Registers are foundational to plant operations and maintenance. When a technician searches for pump documentation two years after commissioning, the register determines whether they find what they need or hit a dead end. The quality of this register directly impacts operational efficiency for the life of the facility.

Commissioning teams use these registers to verify documentation completeness before handover. Operations teams rely on them daily to access equipment specifications, maintenance procedures, and vendor manuals. Auditors trace equipment back to design basis documents through these registers.

Getting the register wrong creates downstream problems: commissioning delays when documentation gaps are discovered late, incomplete handovers that frustrate operations teams, maintenance technicians unable to find critical information, and audit findings that require expensive retroactive documentation efforts.

Getting it right without burning weeks of document control time and risking human error is what Plinth solves. The system ensures completeness, accuracy, and traceability while freeing document controllers to focus on coordination and quality assurance rather than tedious data extraction.

Beyond Tag Registers

The same approach extends to other documentation challenges that follow the same pattern: critical data trapped in technical drawings, requiring tedious manual extraction, and prone to error at scale.

• Equipment List Extraction. Automatically compile comprehensive equipment lists from engineering drawings, vendor documents, and specifications. Cross-reference equipment across disciplines and identify discrepancies.
• Cable Schedule Compilation. Extract cable data from electrical drawings, termination diagrams, and loop drawings. Generate complete cable schedules with source-to-destination mapping and technical specifications.
• Instrument Index Generation. Parse P&IDs and instrument specifications to create comprehensive instrument indexes. Link each instrument to its data sheets, loop drawings, and installation details.
• Drawing Cross-Reference Matrices. Automatically generate cross-reference matrices showing relationships between different drawing types. Track equipment appearances across P&IDs, GAs, details, and vendor drawings.

Implementation Approach

The system was deployed in phases, starting with a pilot on a subset of drawings to validate accuracy and tune the extraction models. The document control team provided feedback on initial results, which was used to refine tag recognition patterns and exception handling rules.

After pilot validation, the system processed the full document set while the team continued with manual extraction in parallel as a quality check. This parallel run confirmed the system's reliability and gave the team confidence to transition fully to the automated approach on subsequent projects.

Each project improves the system. Tag patterns learned from one project enhance accuracy on the next. The machine learning models continuously evolve, adapting to new drawing conventions and vendor standards without requiring manual reprogramming.

If your team is spending weeks on manual extraction work, dealing with documentation bottlenecks on critical path schedules, or struggling with quality issues in engineering registers, Plinth can help. We automate the tedious parts while keeping humans in control of quality and decision-making.