Azati's Engineering Document Intelligence turns complex engineering drawings, including P&IDs (Piping and Instrumentation Diagrams), piping isometrics, and other technical schematics, into structured engineering data that teams can trace, validate, and use.
Unlike generic OCR and document extraction tools, it connects identified equipment, tags, lines, and off-page references into a queryable representation of engineering relationships. This helps teams investigate discrepancies, perform supported cross-sheet tracing, and prepare verified information for downstream engineering and asset-management workflows.
Built for complex industrial archives, the approach can accommodate legacy drawings, scanned documents, and dense visual layouts, subject to source quality and assessment.
Discuss your engineering document workflow
Engineers aren’t becoming smarter. Plants are becoming bigger.
The scale of modern industrial facilities has outgrown manual drawing review. This doesn’t try to make engineers faster at reading drawings. It gives them a system that already knows what’s connected to what.
Andrew Babkin
CTO and Co-founder, Azati
An engineering drawing is more than a collection of labels and symbols. Its meaning depends on how equipment, lines, tags, and off-page connectors relate to one another, and how those connections continue across sheets.
Azati's Engineering Document Intelligence connects identified engineering objects and relationships into a structured, queryable representation. Instead of returning isolated text or tags, it supports tracing and checking workflows based on the connections established in the source data.
It is structured engineering information that teams can investigate, validate, and prepare for downstream workflows.
Project drawings describe intended design. The installed plant may differ. On one engagement, a design-vs-built comparison found that four tanks modeled as identical copies in the drawings were actually different once built.
This was one of several discrepancies the comparison surfaced. Catching this during review is inexpensive; discovering it during operation isn’t. Supports engineering handover, brownfield reviews, and reliable records for ongoing operations.
Structured drawing data and connection tracing help teams identify corrosion loops, trace connected equipment, and locate inconsistencies affecting maintenance planning. This reduces the manual tracing work, not replacing engineering assessment.
Where drawing data and procurement or asset records can be linked, defined checks can flag a valve, flange, or component that doesn’t match the specified requirement before it becomes avoidable investigation or rework.
Connect equipment, tags, lines, and relationships from existing 2D drawings into a queryable graph. This is part of the information foundation a digital twin depends on, alongside 3D models or live operational data where those are also in scope.
Many industrial organizations have already digitized their engineering archives. Yet engineers still need to interpret drawings manually to trace lines, understand equipment relationships, check tagging rules, and reconcile records across documents.
OCR and extraction tools identify text, symbols, and attributes. Engineering Document Intelligence connects those elements into a queryable representation of the engineering system, allowing an AI agent to trace relationships and check defined rules against the source data.
The goal is not to replace engineering judgment. It is to reduce the manual work required to assemble evidence, identify inconsistencies, and prepare engineering decisions.
Industrial engineering archives are rarely uniform. They can contain legacy drawings, scanned sheets, dense visual layouts, handwritten engineering annotations, and documents created under different standards and revision practices.
Generic OCR can extract text from a document without preserving the engineering relationships needed to interpret it. Azati's approach combines document processing with engineering-aware recognition and connection tracing to structure relevant information and retain its context.
The required preparation and verification depend on the condition of the archive, the drawing formats, and the intended use of the data.
| Engineering Document Intelligence | Intelligent Document Processing (IDP) |
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Engineering Document Intelligence is the process of turning complex technical drawings into structured, connected, and verifiable engineering information that can be traced, queried, and prepared for use in industrial workflows. It combines visual recognition, engineering-aware data extraction, relationship mapping, and validation. For example, in a P&ID, identifying a valve tag is only one step. Understanding how that valve connects to a line, associated equipment, and off-page references across related sheets is what makes the extracted information useful for engineering investigation. |
IDP provides relevant category context for the document-recognition and extraction stages. Engineering Document Intelligence extends beyond generic text or field extraction by connecting identified engineering objects and relationships into a representation that supports cross-sheet tracing and defined engineering checks. This distinction matters in oil and gas and process engineering, where large drawing archives can contain dense visual layouts, inconsistent revisions, and connections that span multiple sheets. The extent of extraction, tracing, and validation depends on the source material and the scope established for the project. |
AI agents can provide a natural-language interface to supported tracing and checking workflows. The underlying structured data, connection logic, and quality controls (not the chat interface alone) make the results useful for engineering work.
Engineering archives contain information that conventional text extraction may not fully capture. P&IDs, piping isometrics, scanned drawings, symbols, handwritten annotations, and off-page connectors can all carry information needed to understand an engineering system.
Azati's Engineering Document Intelligence uses visual processing to identify relevant drawing content and structure it for indexing, querying, and downstream use. Depending on the drawing and processing scope, this can include tags, symbols, attributes, and connections, not just words on a page.
When the identified relationships have been established and validated, teams can go beyond finding a drawing or matching a tag. They can investigate how equipment and lines connect across related sheets and follow supported results back to the source drawings.
The level of extraction and tracing depends on the source material. Legacy drawings, poor-quality scans, handwriting, and varying drawing conventions should be assessed using representative samples before the supported scope is agreed.
Extracting information from a drawing is only the beginning. Engineering Document Intelligence structures identified objects and relationships, checks them against defined rules, and prepares validated information for the workflows and systems that need it.
Review available P&IDs, piping isometrics, and other technical drawings for volume, condition, format, and coverage.
Identify supported objects, tags, attributes, symbols, and connections, preserving relationships across sheets where they can be established.
Check representative results against known drawing information, expected connections, and defined engineering rules. Flag missing, conflicting, or ambiguous information for further verification.
Store validated information and its relationships in an agreed data structure that supports tracing and downstream use. This may contribute to a digital twin data foundation; a complete digital twin may require additional models or operational data.
Depending on the source formats and target requirements, this may include conversion to supported structured engineering formats such as DEXPI or Proteus XML, as well as agreed mappings and transfer steps.
The available integrations, interfaces, and acceptance criteria depend on the client's environment. Azati establishes these requirements with the engineering team during technical assessment before extending the workflow to a larger archive or additional systems.
Start with the engineering workflow that creates the most friction: tracing connections across P&IDs, reconciling design and as-built records, checking engineering data, or preparing information for asset-management systems.
Azati can assess a representative drawing archive, the relationships that can be established, and the target workflow to determine whether Engineering Document Intelligence is appropriate, or whether a simpler extraction project would be sufficient.
Request an engineering document assessmentThe agent uses the structured engineering data to execute supported tracing and checking tasks. It plans how to retrieve or follow relevant connections, evaluates the results against the request, and links its findings to source drawings so engineers can inspect the evidence.
Allow users to define or refine supported hierarchies and rules through natural-language instructions.
Link relevant objects and paths to the source drawing for engineering review.
Evaluate whether the retrieved path or rule-check output addresses the request; continue when necessary.
Select a retrieval or traversal approach based on the question.
Represent equipment, tags, lines, and connections as a structured graph.
Engineering document intelligence depends on the quality of the source drawings, extracted objects, and inferred connections. Validation should therefore include representative drawings, known connection paths, expected rule-check results, and cases where the available evidence is incomplete or conflicting.
Azati can define these checks with the client's engineering team and make source references available for review. Decisions affecting safety, inspection, compliance, or maintenance remain subject to the client's established approval process.
The starting point is the engineering archive and the workflow you want to improve. Azati assesses the available drawings and extracted data, the consistency of equipment identifiers and connections, and the systems that need to consume or validate the results.
Depending on the source format and target requirements, the implementation can include conversion into supported structured engineering formats, such as DEXPI or Proteus XML. The applicable formats and transformation steps are confirmed during technical assessment.
Available P&IDs, piping isometrics, electrical drawings, HVAC schematics, and existing structured extraction outputs.
The implementation scope is agreed after assessing these dependencies. A focused pilot can validate a representative drawing set and target workflow before the solution is extended to a larger archive or connected to additional systems.
The strongest fit is typically an organization with a substantial drawing archive, recurring engineering-information tasks, and a defined workflow where manual tracing or inconsistent records create meaningful cost, delay, or operational risk.
Oil and gas operators and process-engineering teams manage large archives of P&IDs, piping isometrics, and other technical drawings. Engineering Document Intelligence helps structure the information in these archives so teams can trace supported connections, investigate discrepancies, and prepare verified data for maintenance, asset information management, and downstream engineering systems.
Azati helps structure this information so teams can trace supported connections, investigate discrepancies, and prepare engineering data for maintenance, asset information management, and downstream systems.
The same processing approach can be assessed for other technical drawing workflows in adjacent industries, including energy and construction, depending on document types and project requirements.
A scoping call with an Azati engineer, not a sales rep. Bring a sample drawing or a description of your archive, and we’ll give you a specific view of whether the graph-and-agent approach fits, what it would take, and an honest answer if a simpler extraction project is what you actually need right now.
Request a scoping callEngineering Document Intelligence builds on extracted and structured drawing data. If the archive first needs scanning, OCR, or initial data extraction, that work may require a separate Computer Vision & OCR engagement.
The agent reasons over a purpose-built graph of your specific engineering data; it isn’t a document-search layer with a chat interface bolted on.
The agent proposes; a human disposes. High-consequence outputs (anything feeding inspection, compliance, or safety decisions) are built with human review in the loop, not shipped as an unsupervised black box.
The value compounds with graph size and query frequency. A small, static archive rarely justifies this layer. Static extraction alone may be enough.
Engineering Document Intelligence turns technical drawings such as P&IDs and piping isometrics into structured engineering information that can be queried, traced, and validated. Unlike document extraction alone, it connects identified engineering objects and relationships, supporting cross-sheet tracing, defined rule checks, and preparation of data for downstream engineering workflows.
Intelligent Document Processing (IDP) covers technologies for recognizing and extracting information from documents. Engineering Document Intelligence applies document recognition and extraction to engineering-specific needs, including connecting identified tags, lines, equipment, and off-page references across drawings. The distinction is the engineering context and relationship tracing, not simply text extraction.
Generic OCR and document extraction focus primarily on recognizing text, fields, and other document content. Engineering Document Intelligence also connects identified drawing elements and relationships where the required connections can be established, allowing teams to trace connections instead of working with isolated extracted values.
Legacy engineering documents, scanned engineering documents, and complex visual layouts can be assessed for processing suitability. The required extraction and verification steps depend on image quality, drawing format, handwriting, and the information that needs to be recovered. A representative sample should be evaluated before committing to processing an entire archive.
It can trace connections across sheets where the relevant tags, lines, off-page connectors, and related objects have been identified and linked. This helps preserve context that would otherwise be lost when sheets are processed independently. The achievable coverage depends on the source drawings and the consistency of the connections.
The workflow can include structuring extracted data, storing it in a database, and preparing it for transfer into agreed target systems. The specific interfaces, data mappings, and transfer steps depend on the client's environment. Supported destinations and integration requirements should be confirmed during technical assessment.
Structured engineering data can help maintenance and asset-integrity teams trace connected equipment, investigate corrosion loops, and identify inconsistencies between engineering documents and related records. The results help teams locate information and prioritize discrepancies for review; they do not replace established maintenance procedures or engineering approval.
Where the relevant engineering specifications can be linked to procurement, asset, or installed-equipment records, defined checks can flag potential mismatches, such as a valve or flange that differs from the expected specification. The feasibility of each check depends on the available records, identifiers, and validation rules.
Customer data and proprietary engineering archives are never aggregated across clients by default. Azati maintains strict data isolation standards for every deployment. Any cross-client data usage, model fine-tuning, or aggregate learning requires the client's explicit written permission and a mutually agreed-upon governance approach to data handling and security.
No. The system can extract and structure information, trace supported relationships, and flag results for investigation. Data quality checks and manual verification remain part of the workflow. Decisions affecting safety, inspection, compliance, or maintenance remain subject to the client's engineering approval process.
Azati has experience with engineering-document processing for P&ID data extraction, piping isometrics, electrical one-line diagrams, HVAC schematics, and fire/gas loop diagrams. Connecting information across these disciplines requires consistent object identification and validated relationships between the relevant drawings. The supported drawing types and cross-discipline connections are established during project assessment.
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.
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.
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.
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.
A refining and petrochemical operator needed object detection, tag matching, equipment classification, and attribute extraction across P&ID, fire and gas, HVAC, and electrical one-line drawings. The drawing types differ structurally, so a single template-matching approach could not cover the archive.
Azati built a computer vision and OCR pipeline combining YOLO-based small-object detection with scene text recognition and attribute extraction across four drawing disciplines. A dedicated team spanning development and data annotation delivered the production pipeline over 15 months.
A major Middle Eastern oil and gas operator struggled with manually reviewing and reconciling massive volumes of engineering documents, leading to operational delays and errors. The client required a scalable, automated verification solution that integrated seamlessly with their existing engineering workflows.
Azati developed an AI-powered platform that automates the extraction and validation of technical documents and AutoCAD drawings, seamlessly integrating with the client's Knowledge Hub to streamline reviews without disrupting existing workflows.
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, fast enough to replace a multi-engineer manual process.
Azati built the full pipeline in three months: multi-level visual context analysis for tag classification, equipment connection detection from wiring diagrams, and structured AVEVA Engineering table generation at up to 300 documents per hour.
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