Engineering Document Intelligence
Extracts and connects supported information from P&IDs, piping isometrics, and other technical documents. Relevant data can inform material and asset reconciliation.
Industrial Master Data and MRO AI for Asset-Heavy Operations
Make material and asset records more reliable for maintenance, procurement, and inventory decisions.
Industrial operators often manage material and asset records across ERP systems, maintenance platforms, plant registers, and engineering documents. The same component may appear under different descriptions or codes, equipment identifiers may not align with functional locations, and incomplete records can make inventory decisions harder to validate.
Azati develops industrial data solutions that normalize material master records, identify candidate duplicates, reconcile equipment identifiers with asset hierarchies, and support spare-parts criticality analysis and MRO inventory optimization.
Start with the data you have: existing ERP or CMMS records, engineering-document data where relevant, or a combination of both.
Discuss your MRO data workflow7 years
Continuous development and operation of an inventory management platform for a petrochemical operator.
AI-assisted material normalization
Matching material nomenclature across coding systems to improve visibility across fragmented inventory records.
On-premise delivery
Inventory platform developed and operated within the client’s existing infrastructure.
Why industrial master data remains fragmented
Digitizing engineering documents does not automatically reconcile the material and asset records held in enterprise systems. An ERP or CMMS also does not guarantee consistent descriptions, identifiers, hierarchies, or inventory policies across sites.
For example, a ball valve might appear as “VLV-BALL-2IN-SS,” “Valve, Ball, 2 in Stainless,” and “BALL VLV 2IN SS316.” These records may describe the same item or components with important differences. Their attributes and context need to be checked before records can be treated as duplicates or equivalents.
Inconsistent data can make it harder to:
- Identify duplicate or near-duplicate material records across plants and systems.
- Understand available stock when components are recorded under different descriptions or codes.
- Reconcile equipment tags with functional locations and maintenance hierarchies.
- Apply consistent criticality criteria to spare parts.
- Evaluate inventory using reliable material attributes, consumption history, supplier lead times, and operational context.
The result is less confidence in what is available, what is needed, and where a component belongs. Azati helps address these problems while retaining the client’s existing ERP, CMMS, and engineering platforms where they remain fit for purpose.
Industrial master data as a convergence point
Industrial master data remediation can start with existing ERP, CMMS, inventory, and asset records. Engineering documents can provide additional evidence when equipment identifiers or material attributes need to be extracted or verified.
Azati’s Industrial Master Data & MRO AI capability reconciles the relevant information into more consistent, reviewable records for maintenance, procurement, and inventory workflows.
Industrial Master Data & MRO AI
Normalizes material records, identifies candidate duplicates, reconciles equipment identifiers with functional locations or asset hierarchies, and supports criticality and inventory analysis.
Engineering data for digital twins
Prepares structured engineering information as part of a broader digital-twin data foundation, which may also require 3D models, spatial information, operational data, and integrations.
These capabilities can be combined when they serve the same operational objective, but they are not a mandatory sequence. A master-data initiative can start with ERP exports; engineering-document extraction can add context where needed; and a digital-twin program may require a broader scope.
The goal is to make the relevant data more consistent, traceable, and usable without imposing a new system architecture.
Four connected capabilities for industrial master data and MRO
Azati scopes these capabilities around the client’s source data, operational priorities, and target workflows. They can be delivered together or as a focused workstream.
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Material master data cleansing and nomenclature normalization
The challenge: The same or similar materials may appear under different descriptions, codes, units, or attribute conventions across plants, suppliers, and legacy systems. This fragments inventory visibility and makes duplicate detection difficult.
How Azati can help: Use attribute-based matching and AI-assisted normalization to identify candidate duplicates, compare material descriptions, and organize records around an agreed nomenclature and taxonomy. Matching rules can account for relevant characteristics such as dimensions, material grade, manufacturer, and part number where those attributes are available and reliable.
Potential outputs include:
- Standardized material descriptions and attribute formats
- Candidate duplicate groups with supporting match information
- Records flagged for missing, conflicting, or insufficient attributes
- A reviewable mapping between existing records and the proposed normalized records
- Data exports or integration components for the agreed target environment
The objective is to improve the consistency of the material register while preserving the information needed for engineering, procurement, and maintenance review. Records should not be merged solely because their descriptions look similar: the acceptance criteria must reflect the material attributes that distinguish interchangeable from non-interchangeable parts.
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Equipment-tag and functional-location mapping
The challenge: Equipment tags in engineering documents may not align cleanly with functional locations, asset hierarchies, or maintenance records. Differences in naming conventions, missing identifiers, and legacy changes can make relationships difficult to establish.
How Azati can help: Match available equipment tags and asset identifiers to functional locations or asset records using agreed rules and source references. Engineering-document extraction can supply useful context when available, but it is not a prerequisite if the relevant records already exist digitally.
Potential outputs include:
- Proposed mappings between engineering tags and functional locations or asset records
- Parent-child relationships for review within the target asset hierarchy
- Records with missing, conflicting, or ambiguous identifiers
- Source references and validation status where supported by the data and implementation
- Agreed mappings and data-transfer components for the target system
The terminology and data model depend on the environment. SAP functional locations, for example, should not be treated as interchangeable with every location or asset object in another CMMS. Mapping rules must reflect the client’s actual hierarchy and maintenance model.
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Spare-parts criticality analysis
The challenge: Criticality classifications may be inconsistent across sites or rely heavily on local experience. Teams can struggle to justify which spares warrant special stocking policies and which can follow routine replenishment rules.
How Azati can help: Structure and evaluate spare-parts criticality using agreed criteria. Typical factors include what the equipment does, what happens if it fails, how often it’s failed before, whether a substitute exists, and how long a replacement takes to source. The specific list is agreed with the client’s reliability and maintenance teams.
Potential outputs include:
- A configurable criticality framework and documented scoring criteria
- Candidate criticality tiers with supporting factors
- Identification of missing data that limits classification confidence
- Review queues for disputed or safety-sensitive classifications
- Structured outputs for use in agreed maintenance and inventory workflows
The method should be agreed with the client’s reliability and maintenance teams. A reliability-centered maintenance-informed approach may be appropriate where it fits the client’s methodology, but the exact framework and inputs should be established during discovery.
AI can help organize evidence and apply consistent rules; it should not independently make safety-critical maintenance or stocking decisions.
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MRO inventory optimization and stock visibility
The challenge: Inconsistent material records make it harder to see the true inventory position. Duplicate records can fragment stock visibility, while weak criticality classifications or incomplete supplier data can lead to unsuitable replenishment decisions.
It’s a common pattern: three plants each keep “just in case” stock of what looks like the same bearing, because nobody can confirm from the records alone whether they’re actually interchangeable. Multiply that by a few thousand SKUs, and the padding adds up to real capital sitting in three warehouses instead of one.
How Azati can help: Combine normalized material records with available stock balances, consumption history, supplier lead times, and agreed stocking policies to support more informed inventory analysis. Depending on data quality and scope, this can include identifying potential surplus, highlighting stockout exposure, and proposing replenishment parameters for review.
Potential outputs include:
- A more consistent view of material records and associated stock
- Candidate duplicate or surplus records for inventory review
- Visibility into selected critical spares and stockout risks
- Replenishment recommendations based on agreed policies and available data
- Reports or integrations for procurement and inventory workflows
Inventory optimization is not simply about reducing stock. The goal is to identify where stock may be unnecessary while preserving the availability requirements of critical equipment. Any proposed change should be evaluated against demand patterns, supplier constraints, maintenance needs, and the consequences of a stockout.
Have a material-data problem to scope?
Whether the issue is duplicate material records, inconsistent asset hierarchies, or limited visibility into spare-parts inventory, the starting point is understanding the data and decisions involved. Azati can help define the relevant data sources, validation requirements, and a practical scope for remediation.
Discuss your MRO data workflowHow the data fits together
The workflow depends on the data available and the operational problem to solve.
Possible inputs
- Material masters and stock records from SAP MM or other ERP environments.
- Asset, maintenance, and location records from SAP, Maximo, or comparable systems.
- Engineering tags and attributes extracted from drawings, where available.
- Legacy spreadsheets, vendor catalogs, bills of materials, and approved reference data.
Reconciliation and analysis
- Normalize descriptions and selected attributes.
- Identify candidate duplicates and conflicting records.
- Match equipment identifiers to asset hierarchies.
- Apply agreed criticality and inventory-analysis rules.
- Flag uncertain cases for review.
Possible outputs
- A normalized material register.
- Candidate duplicate groups and proposed mappings.
- Equipment-to-location relationships with validation status where supported.
- Criticality classifications with supporting rationale.
- Inventory analysis and replenishment recommendations.
- Validated data exports or integration components for agreed systems.
The resulting data can support maintenance, procurement, inventory analysis, and digital-twin initiatives. Required outputs and integrations depend on the source data, target systems, and workflows in scope.
Built around engineering review and data governance
Material and asset data can affect purchasing, maintenance planning, and critical-spare availability. A false duplicate match or incorrect criticality classification can create operational risk, so proposed changes need clear validation and approval.
Azati can design workflows in which:
| AI supports reconciliation | Matching and classification components identify candidate relationships and inconsistencies for review |
|---|---|
| Validation rules check defined requirements | Agreed constraints and reference data flag invalid or conflicting values |
| Uncertain cases remain visible | Missing attributes, ambiguous matches, and conflicting source records can be routed to the relevant data owner |
| The client controls approval | The client’s approval process remains in place for material merges, hierarchy changes, criticality classifications, and production-system updates |
| Changes remain traceable where the implementation supports it | Source references, proposed changes, validation status, and review outcomes can be retained according to the agreed data model and audit requirements |
The purpose is to make the reconciliation process more consistent and reviewable while keeping consequential decisions under appropriate engineering, maintenance, and procurement governance.
How an engagement works
Assess the source data and operational problem
Review a representative material master, asset register, or inventory extract. Identify the key inconsistencies, available attributes, source systems, and business decisions affected by the data.
Define matching and decision rules
Agree how materials should be compared, which identifiers establish asset relationships, what criteria govern criticality, and which records require manual review.
Validate on representative records
Test the proposed normalization, matching, mapping, or classification approach against a representative sample. Review false matches, unresolved cases, missing data, and the effort needed for human approval.
Prepare the target workflow
Agree on the required outputs, integration approach, data ownership, approval responsibilities, and validation criteria. Begin with the selected scope before extending the process to additional sites, catalogs, or systems.
A sample assessment can help determine whether the available data supports the proposed approach and what further validation may be needed. It does not, by itself, establish the full implementation effort, guarantee inventory reduction, or confirm integration readiness. Scope, deliverables, and data-handling arrangements should be agreed before sample files are transferred.
Who benefits from Industrial Master Data & MRO AI?
This capability is designed for asset-heavy organizations where inconsistent material and asset records affect maintenance, procurement, and inventory decisions.
Maintenance and reliability leaders
Need consistent spare-parts classifications and a defensible basis for reviewing stocking policies.
MRO procurement and supply chain leaders
Need better visibility into duplicate material records, unclaimed stock, supplier lead times, and inventory exposure.
Master-data and enterprise application owners
Manage material master quality, ERP or CMMS records, asset hierarchies, and the downstream impact of data changes.
Asset-heavy industrial operators
Oil and gas, petrochemical, utilities, and process manufacturing organizations with large or multi-site catalogs and recurring data-reconciliation work.
Industrial digital-twin and data-program owners
Need more consistent material and asset records as one part of a broader data foundation.
When is this the right fit and when is it not?
A strong fit
- Material records are duplicated or inconsistently described across sites, catalogs, or systems.
- Asset identifiers and functional locations are difficult to reconcile.
- Criticality analysis depends on inconsistent data or manual cross-referencing.
- Inventory decisions are constrained by fragmented material records or incomplete visibility.
- The organization needs to improve data quality while retaining its existing enterprise platforms.
When another approach may be sufficient
- The material catalog is small, consistent, and already governed effectively.
- The main problem is physical stock counting rather than master-data quality.
- The organization expects AI to make final safety-critical or procurement decisions without review.
- There is no agreed data owner or process for approving changes.
- The requested result depends on information that is unavailable and cannot be reasonably reconstructed or verified.
A focused data-quality or inventory analysis may be sufficient for some organizations. The initial assessment should establish whether a larger MRO data initiative is justified.
What makes Azati’s approach different?
Industrial master data requires more than standardizing text fields. Material attributes determine whether components are genuinely equivalent; equipment identifiers may need to be reconciled across engineering documents and maintenance hierarchies; and spare-parts criticality must reflect operational criteria.
Azati combines AI-assisted matching with industrial software engineering to address these relationships in the context of the client’s existing data and systems. Depending on the use case, the work can connect material normalization, asset hierarchy reconciliation, and MRO inventory analysis.
Rather than assuming that the client needs to replace its current platforms, Azati scopes the required data workflows, validation logic, and integration components around the target environment and its governance requirements.
See how Azati’s P&ID agent works
Frequently asked questions
Industrial master data management for MRO focuses on making material, spare parts, equipment, and related asset records consistent and usable across maintenance and procurement workflows. Depending on the scope, this can include nomenclature normalization, duplicate detection, equipment-to-location mapping, criticality analysis, and inventory optimization.
No. The work can start with material masters, asset registers, inventory exports, and other existing digital records. Engineering Document Intelligence can provide additional information when relevant equipment tags or attributes need to be extracted from technical drawings, but it is not a prerequisite for every master-data initiative.
Azati can assess material, inventory, and asset records from SAP, Maximo, or comparable environments and define the required normalization and reconciliation workflow. The relevant data objects, interfaces, mappings, and transfer method depend on the client’s configuration and the agreed scope. Compatibility should be confirmed during technical assessment.
AI-assisted matching can compare material descriptions and available attributes to identify candidate duplicates or equivalent items. The matching criteria must reflect the characteristics that distinguish interchangeable parts from similar but incompatible components. Ambiguous cases should be flagged for review rather than merged automatically.
Criticality is determined using a defined framework agreed with the client’s maintenance and reliability specialists. Depending on the methodology, relevant factors may include equipment function, failure consequences, historical failures, replacement lead time, and substitute availability. AI can help structure the evidence and apply consistent rules, while final classifications remain subject to the client’s approval process.
Better material normalization and inventory visibility can help identify potential duplicate records, surplus stock, and inconsistencies that affect replenishment decisions. The impact depends on the quality of the source data, demand patterns, supplier lead times, stocking policies, and operational criticality. Proposed changes should be validated against those constraints rather than optimizing for stock reduction alone.
The workflow can prepare normalized records, mappings, and recommendations for transfer into agreed systems. Whether updates are automated, staged, or handled through controlled exports depends on the target environment and governance requirements. Material merges, hierarchy changes, and other consequential updates should follow the client’s approval process.
Yes. A more consistent material and asset registry can contribute to the data foundation for a digital twin. A complete digital twin may also require engineering relationships, 3D models, spatial information, operational data, and other integrations. The necessary scope depends on the intended digital twin use case.
Data use depends on the agreed engagement and applicable confidentiality and data-handling requirements. Any use of client material or asset data beyond the authorized project scope should require explicit agreement. Data access, isolation, and any model-development arrangements should be documented before work begins.
Assess your MRO data readiness
Start with a representative material master, inventory extract, or equipment register. Azati can review the available data and discuss whether it supports the intended normalization, reconciliation, criticality, or inventory-analysis use case.
Before sample files are transferred, agree on the assessment scope, expected deliverables, and data-handling arrangements.
Request an MRO data assessmentThe initial assessment can help clarify:
- Which relevant attributes are available and reliable.
- Whether potential duplicate records can be distinguished with the available data.
- Which equipment-to-location relationships can be established or need further review.
- What validation, integration, and governance work may be required next.
Azati’s relevant industrial data projects
Industrial master data work often depends on information spread across engineering documents, material registers, and enterprise systems. These Azati engagements demonstrate related capabilities in engineering data reconciliation, extracting equipment identifiers, and preparing structured data for downstream workflows.
AI-Powered Petrochemical Inventory Management Platform
Business challenge
A large petrochemical operator faced high carrying, maintenance, and disposal costs from unclaimed and duplicate inventory across multiple ERP systems using different coding conventions. The same material was often tracked under different codes, making it impossible to see true inventory levels without manual reconciliation.
Solution at a glance
Azati ran a seven-year dedicated team engagement building and operating an on-premise inventory management platform with AI-assisted nomenclature normalization across disconnected ERP systems, covering backend development, DevOps, and test automation alongside the core product functionality.
How Azati solved the challenge
- AI-assisted nomenclature normalization: ML components matching and normalizing materials coded differently across legacy ERP systems.
- Inventory search and scenarios: Search across warehouse and MRO stock, custom scenario creation, and material scope definition.
- Dashboard reporting: Stock level and valuation visibility without raw data access.
- On-premise DevOps: Kubernetes, Helm, and Keycloak deployment on the client’s infrastructure.
- Test automation: Playwright, pytest, and httpx-based framework covering UI, API, and database validation.
Business outcomes
- Lower inventory carrying costs: AI-matched nomenclature gave the business a unified view of true inventory across previously fragmented ERP records.
- Seven years without a vendor switch: This was the client’s first and only outsourcing relationship for this system, reflecting sustained trust in the engagement.
- Industrial inventory expertise inside Azati: Deep expertise in MRO inventory optimization, ERP normalization, and supply chain integration for the petrochemical sector.
AI-Powered Piping Isometric Digitization, Flange Register Consolidation
Business challenge
A global energy company needed to create a single, reliable flange register by consolidating information scattered across approximately 250,000 piping isometrics, CAD drawings, and marked-up inspection documents produced by more than ten engineering contractors.
The documentation varied significantly in format, quality, and annotation standards, making manual reconciliation impractical and error-prone. Differences in drawing layouts, handwritten notes, inconsistent page structures, and varying engineering conventions prevented reliable alignment between CAD drawings and marked-up documentation. Without a unified, traceable dataset, flange management, inspection planning, and integrity management remained highly labor-intensive.
Solution at a glance
Working as Petronas's AI engineering partner, Azati developed a customized computer vision and machine learning pipeline that automatically extracted engineering data, identified flanged joints, mapped CAD and marked-up drawings, and generated a consolidated flange register.
Rather than relying on off-the-shelf OCR, the solution used trainable recognition models and custom machine learning components tailored to engineering documentation, enabling accurate extraction across highly inconsistent datasets and providing a scalable foundation for future processing of engineering documentation.
How Azati solved the challenge
- AI-powered engineering document processing: Developed custom OCR and computer vision models capable of identifying flanged joints, interpreting piping topology, and extracting engineering attributes from both CAD drawings and scanned, marked-up isometrics with varying document quality.
- Cross-document reconciliation: Built intelligent mapping logic that automatically aligned CAD drawings with contractor markups despite differences in layouts, page structures, drawing orientation, and engineering conventions, creating consistent links between both document sources.
- Automated data enrichment: Extracted and assigned engineering attributes, including flange identifiers, pipe specifications, materials, and pipe sizes, creating complete and standardized flange records from fragmented source documentation.
- Scalable processing pipeline: Designed a production-grade AI pipeline capable of processing very large engineering document collections efficiently while continuously adapting to newly discovered document variations through iterative model improvements.
- QA/QC verification support: Delivered a verification interface allowing engineering teams to review mapped flanges alongside extracted metadata, accelerating quality assurance while simplifying validation and issue resolution.
Business outcomes
- Unified engineering data: Created a single, consistently tagged flange register linked across CAD and marked-up documentation, providing a trusted source of engineering information.
- Accelerated project delivery: Reduced the overall project schedule by 70% by automating document reconciliation and eliminating large-scale manual engineering activities.
- Lower engineering effort: Reduced manual processing requirements by approximately 50%, allowing engineering specialists to focus on higher-value inspection and integrity activities.
- Improved traceability and quality: Increased consistency, auditability, and confidence in flange information by automatically reconciling engineering data across multiple contractors and document sources.
- Scalable foundation for asset integrity management: Delivered an AI-driven processing framework capable of supporting future inspection planning, maintenance programs, and engineering documentation initiatives across additional assets and projects.
AI-Assisted Engineering Drawings Digitization
Business challenge
A large industrial operator needed to extract flange and engineering data from low-quality legacy P&IDs and piping isometrics to prepare Flange Master Registers (FMR). Degraded scans, inconsistent layouts, handwritten annotations, and ambiguous graphical elements made conventional OCR and fully autonomous AI unreliable.
Solution at a glance
Azati built a hybrid engineering document intelligence workflow combining computer vision, multimodal AI, deterministic validation, and human engineering QA. The solution identified flange joints, interpreted engineering annotations, structured the extracted data, and prepared validated FMR outputs while keeping engineers in control of ambiguous cases.
How Azati solved the challenge
- AI-powered flange identification: Used object detection to identify flange joints across archived piping drawings, assign unique IDs, and generate engineering markups, including for low-quality scans.
- Multimodal engineering document understanding: Combined OCR and multimodal AI to interpret engineering annotations, tables, and contextual information within drawings and convert the results into structured data.
- Context-aware data extraction: Analyzed drawing regions and engineering context rather than relying on text extraction alone, enabling the workflow to handle fragmented layouts and non-standard documentation.
- Deterministic validation and normalization: Applied rules-based validation to detect inconsistent outputs, filter noise and hallucinations, normalize extracted information, and maintain data consistency.
- Human engineering QA: Designed a human-in-the-loop workflow in which engineers review and correct ambiguous cases instead of manually processing every drawing from scratch.
- Flange Master Register preparation: Combined validated flange identification and engineering metadata into structured outputs suitable for FMR preparation and downstream maintenance, procurement, and asset-integrity workflows.
Business outcomes
- 2x faster engineering review: AI-assisted extraction and validation accelerated flange identification and FMR preparation, reducing the time required for engineering review.
- 45% less manual interpretation: The hybrid workflow automated repetitive document analysis while allowing engineers to focus on exceptions and engineering validation.
- 471 drawings processed: The solution demonstrated automated processing of 471 engineering drawing pages while maintaining a controlled validation workflow.
- Better maintenance readiness: Structured and validated engineering information reduced documentation bottlenecks for maintenance and shutdown planning.
- Reusable foundation for engineering document modernization: The project established a practical workflow for applying AI to additional legacy engineering documentation and future engineering data modernization initiatives.
Automated Tag Extraction and AVEVA Table Generation from Engineering Drawings
Business challenge
A large oil and gas engineering operator needed tag data extracted and classified from engineering drawings, categorized for AVEVA Engineering tables, and equipment connections identified from wiring diagrams. The manual process required multiple engineers and could not keep pace with document volume.
Solution at a glance
Azati built a full pipeline from data annotation to production that extracts and classifies tags, identifies equipment connections, and generates structured AVEVA Engineering tables, delivered in three months.
How Azati solved the challenge
- Tag extraction and classification: Identifies tags on engineering drawings and classifies them using visual context.
- Equipment connection identification: Detects connections between equipment from wiring diagrams.
- AVEVA table generation: Produces structured tables ready for AVEVA Engineering.
Business outcomes
- 300 documents per hour: The pipeline processes up to 300 engineering documents per hour.
- Three months to production: The full MLOps pipeline, from data annotation to deployment, was delivered in three months.
AI-Powered Engineering Drawing Digitization and DEXPI Conversion
Business challenge
An oil and gas operator struggled to utilize 35,000 static engineering drawings (PEFS) due to fragmented file formats (AutoCAD, PDF, TIFF), which hindered searchability, process tracing, and system integration. Manual reviews caused significant operational delays, creating a need for a scalable, automated solution to convert these documents into structured, machine-readable data while preserving engineering relationships and industry standards.
Solution at a glance
Azati’s AI-powered platform automates PEFS drawing processing, converts data to the DEXPI standard, and validates results for system integration. This transforms static archives into a searchable knowledge base that improves decision-making, streamlines workflows, and supports future digital twin initiatives.
How Azati solved the challenge
- Multi-format engineering document processing: Developed an AI pipeline capable of processing engineering drawings from AutoCAD, PDF, TIFF, and JPEG formats, creating a unified workflow regardless of the original document source.
- Engineering data extraction: Applied computer vision and OCR to identify equipment, instruments, engineering tags, and process elements while automatically associating textual information with corresponding engineering objects.
- Process topology reconstruction: Built intelligent algorithms that reconstruct relationships between engineering components, transforming disconnected drawing elements into structured representations of process networks rather than simple collections of extracted symbols.
- DEXPI-standard conversion: Converted engineering data into DEXPI-compliant models with automated validation, enabling direct integration with engineering platforms, asset management systems, and future digital engineering initiatives.
- Continuous quality improvement: Implemented an iterative review and model refinement process that continuously improves extraction accuracy as additional engineering drawing types and conventions are processed.
Business outcomes
- Searchable engineering knowledge: Converted approximately 35,000 engineering drawings into structured engineering information, enabling engineers to locate equipment, process relationships, and engineering assets without manually reviewing thousands of documents.
- Reduced engineering workload: Pilot results demonstrated an estimated 60-80% reduction in manual engineering effort by automating extraction, classification, topology reconstruction, and standards-based conversion.
- Higher engineering data quality: Delivered validated engineering information in an industry-standard format, improving consistency, traceability, and confidence in engineering documentation.
- Integration-ready engineering data: Produced DEXPI-compliant outputs that can be consumed directly by engineering platforms, enterprise asset management systems, and other downstream applications without requiring extensive custom transformation.
- Foundation for digital engineering: Established the structured engineering data layer required to support future initiatives such as digital twins, predictive maintenance, engineering analytics, and intelligent asset management.
