How to create a single source of truth for flange management across hundreds of thousands of engineering drawings?
Azati partnered with Petronas to help a global energy company modernize engineering information by transforming fragmented piping documentation into a trusted data foundation. Using custom AI, trainable OCR, and engineering-aware computer vision, the solution reconciled more than 250,000 piping isometrics into a single flange register supporting inspection, maintenance, asset integrity, and future digital engineering initiatives.
piping isometrics automatically processed and reconciled
reduction in project schedule
reduction in manual engineering effort
Large industrial operators depend on accurate engineering documentation to manage inspections, maintenance, regulatory compliance, and asset integrity. When engineering records become fragmented across CAD files, contractor markups, and inspection reports, organizations lose a trusted source of information, increasing operational risk, slowing capital projects, and making maintenance planning significantly more expensive.
The client needed to establish a consolidated flange register. That is, a single record linking every flanged connection to its design documentation, inspection history, and engineering attributes. This register serves as the foundation for inspection planning, shutdown preparation, maintenance activities, regulatory reporting, and long-term asset integrity management.
Manual reconciliation was impractical due to the scale of documentation and the inconsistencies introduced by multiple contractors, varying drafting standards, and handwritten annotations.
Working together with Petronas, Azati developed a customized AI-powered processing pipeline capable of extracting, matching, validating, and consolidating engineering information into a unified flange register supporting inspection, maintenance, and integrity management.
Azati designed a specialized AI solution capable of understanding engineering drawings rather than simply extracting text. The platform combined trainable OCR, computer vision, topology analysis, and custom machine learning models to reconcile asset lifecycle information across highly inconsistent documentation while preserving traceability back to sources.
Azati modernized engineering information by transforming fragmented documentation into structured, validated engineering data while preserving traceability back to original CAD and marked-up drawings. Rather than replacing existing systems, the project established a trusted information layer that supports inspection, maintenance, brownfield modernization, and future digital engineering initiatives.
Flange information existed across CAD drawings, marked-up scans, inspection reports, and contractor documentation without a reliable mechanism for consolidation.
More than ten contractors produced drawings using different templates, drafting conventions, symbols, title blocks, and annotation styles.
Engineering specialists previously relied on manual comparison of drawings to validate flange information, making large-scale reconciliation slow and error-prone.
The client required a reusable process capable of supporting future inspection planning, maintenance activities, and integrity management.
Azati combined advanced AI engineering expertise with Petronas's deep understanding of piping documentation and flange management. Rather than relying on generic OCR technologies, the team designed custom trainable recognition models and engineering-specific computer vision components capable of handling highly variable industrial documentation at enterprise scale.
Recognition models were trained specifically for piping isometrics, engineering notation, contractor markups, and real project documentation.
Previous successful engagements within the energy sector reduced delivery risk for another large-scale engineering documentation initiative.
Unlike conventional OCR platforms designed to extract text, Azati developed an engineering-aware AI capable of interpreting piping topology, contractor-specific engineering notation, and relationships between CAD and marked-up documentation. This enabled reliable reconciliation of engineering information that conventional document processing platforms could not achieve.
The processing pipeline was tailored to the client's engineering validation processes, output formats, and QA/QC requirements rather than forcing standardized AI models into existing workflows.
The solution was designed to process hundreds of thousands of engineering documents while maintaining consistent quality and traceability.
Enterprise engineering modernization begins with trusted information. Azati helps industrial organizations transform fragmented engineering documentation into structured data that supports asset integrity, brownfield modernization, engineering analytics, and future AI initiatives.
Discuss your engineering AI initiativeAzati developed custom computer vision and engineering-aware AI capable of matching CAD drawings with highly inconsistent marked-up documentation produced by multiple contractors. The approach created reliable mappings across more than 250,000 engineering drawings while preserving full traceability back to the original documentation.
Rather than simply digitizing drawings, the project modernized engineering data by converting disconnected documentation into a structured information asset. The solution combined document understanding, computer vision, engineering topology analysis, and automated reconciliation to create a single flange register supporting multiple downstream engineering processes.
Although developed for piping isometrics, the same AI techniques can be applied to P&IDs, engineering drawings, inspection records, and other technical documentation.
The platform automatically analyzed piping isometrics, identified flanged joints, interpreted engineering topology, and extracted engineering attributes from both CAD drawings and scanned markups.
Custom computer vision models matched CAD drawings with contractor markups despite differing layouts, scan quality, and document structures.
The platform assigned unique identifiers to every flange and consolidated engineering information into a consistent register linked to all source documentation.
Missing engineering attributes were automatically assigned using extracted information and engineering context.
Azati developed a review interface that enables engineering specialists to validate AI-generated results by synchronizing the visualization of drawings with extracted engineering data.
The production-grade processing pipeline was designed to efficiently process millions of engineering documents while supporting continuous optimization and future engineering initiatives.
Azati was responsible for designing and implementing the AI pipeline, custom OCR models, computer vision algorithms, and production processing infrastructure, while Petronas provided domain expertise in flange management and engineering documentation.
| Area | Azati contribution |
|---|---|
| AI engineering | Developed custom trainable OCR and ML models |
| Computer vision | Built drawing comparison and reconciliation algorithms |
| Engineering data extraction | Automated flange identification and metadata extraction |
| Data consolidation | Generated a unified flange register |
| QA/QC | Developed an engineering validation interface |
| Platform engineering | Built a scalable production processing pipeline |
| Delivery | Iterative Agile development with continuous model improvement |
Azati worked with one of the most challenging aspects of industrial AI: engineering documentation created by multiple organizations using inconsistent drafting standards over many years. Success depended on solving engineering-specific edge cases that conventional OCR solutions could not reliably address.
Marked-up drawings varied significantly in templates, symbols, scan quality, handwritten annotations, and drafting practices.
Some contractor drawings required non-trivial geometric transformations rather than simple image rotation to correctly align piping structures.
CAD drawings and contractor packages frequently divided engineering content differently, requiring intelligent cross-page matching.
Rather than replacing engineering experts, Azati developed a dedicated validation workspace allowing QA/QC specialists to review AI-generated flange mappings, compare synchronized CAD and marked-up drawings, flag discrepancies, and approve engineering data before publication. This accelerated validation while preserving engineering confidence in the final dataset.
New document variations and engineering edge cases required iterative optimization throughout the project.
Azati helps engineering organizations automate document processing, digitize technical records, and transform legacy engineering documentation into trusted digital assets.
Talk to an industrial AI expert| Metric | Value |
|---|---|
| Engineering drawings processed | 250,000+ |
| Project duration | 12 months |
| Schedule reduction | 70% |
| Manual effort reduction | 50% |
| Delivery model | Dedicated partner team |
| AI approach | Custom trainable OCR and computer vision |
Azati transformed fragmented engineering documentation into a trusted digital engineering asset that supports inspection, maintenance, and integrity management. The unified flange register became the operational reference for downstream inspection and maintenance workflows. The project reduced manual reconciliation while improving engineering traceability, auditability, and long-term operational readiness.
Engineering teams gained a consolidated flange register consistently linked to both CAD and marked-up documentation.
Automation dramatically reduced the time required to consolidate and validate engineering documentation across large projects.
Automated reconciliation reduced manual errors while improving consistency across engineering records.
Engineers could validate AI-generated results using dedicated review workflows rather than manually comparing thousands of drawings.
Brownfield modernization initiatives often begin with fragmented engineering documentation accumulated over decades of operations. By creating a trusted flange register linked to both CAD and marked-up drawings, Azati established reliable engineering information that can reduce manual document reconciliation during plant upgrades, shutdown planning, and asset modification projects.
The consolidated engineering dataset supports future brownfield modernization, engineering analytics, asset lifecycle management, and industrial AI initiatives.
The organization established a reliable engineering dataset spanning hundreds of thousands of documents without relying on manual reconciliation.
Modernizing existing industrial facilities requires accurate engineering records before equipment can be upgraded, inspected, or modified safely. The consolidated flange register provides a trusted engineering baseline that reduces documentation risk during future brownfield projects.
Digital engineering initiatives depend on trusted information. By reconciling engineering documentation into a consistent digital dataset, the project established reliable engineering data that future analytics, engineering systems, and industrial AI initiatives can build upon.
Engineering expertise was previously embedded across hundreds of thousands of disconnected documents produced by different contractors. The project transformed this fragmented engineering knowledge into structured, traceable information that engineering teams can search, validate, and reuse throughout the asset lifecycle.
A centralized, validated engineering dataset reduced the operational risks associated with inconsistent contractor documentation, improving confidence in engineering decisions throughout the asset lifecycle.
Centralized, validated engineering information accelerated planning, inspection, and operational activities.
Engineering specialists could focus on higher-value technical activities instead of repetitive document comparison.
Azati worked as Petronas's AI engineering partner, combining machine learning expertise with deep engineering domain knowledge.
The project followed Agile delivery with continuous refinement of recognition models based on newly discovered document variations and engineering edge cases.
The engagement combined machine learning engineers, computer vision specialists, data annotation experts, full-stack developers, and technical leadership to deliver a production-grade engineering document intelligence solution.
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