Verdantis’ AI-driven Intelligent Document Processing (IDP) solution automates the extraction and integration of data from complex documents like BOMs, work orders, and technical manuals. By supporting 2D/3D file formats and offering seamless ERP system integration, our platform ensures accurate, real-time updates while reducing manual effort and errors.
Unstructured documents continue to pose a significant challenge. From BOMs and work orders to technical drawings and equipment manuals, over 80% of business-critical information is still locked in formats not readily digestible by traditional systems. This is where Intelligent Document Processing (IDP) comes into play—an emerging solution that transforms unstructured content into structured, actionable data.
Unlike basic OCR solutions that merely digitize text, IDP leverages a fusion of Artificial Intelligence (AI), Machine Learning (ML), Natural Language Processing (NLP), and Robotic Process Automation (RPA). These technologies work together to not only extract data but also understand and validate its meaning within a business context. The goal isn’t just automation—it’s intelligence: capturing insights and patterns from documents that drive smarter decisions and improved operational workflows.
Intelligent Document Processing (IDP) is a powerful solution that combines Artificial Intelligence (AI), Optical Character Recognition (OCR), Machine Learning (ML), and Natural Language Processing (NLP) to extract structured data from unstructured documents. Unlike traditional data entry or basic OCR tools that merely convert scanned images into text, IDP understands the context, structure, and semantics of a document—enabling automation of complex, document-heavy workflows.
For asset-intensive industries, many of the most valuable data sources are locked inside technical documents like Engineering Design Specifications (EDS), Bills of Materials (BOMs), Work Orders, and Maintenance Manuals. These documents are often in PDF or scanned format, vary widely in layout, and contain domain-specific terminology. Traditional automation tools struggle to extract relevant, usable data from such sources. IDP systems trained on industry-specific language models, however, can automatically identify and extract key attributes like part numbers, equipment specs, tolerances, vendor references, and work instructions.
First, the system ingests documents from various physical sources—hard copies such as EDS (Equipment Data Sheets), BOMs (Bill of Materials), Work Orders, and technical manuals—which are scanned and digitized for processing. It uses intelligent classification models to determine document types (e.g., EDS vs. BOM) and routing logic for extraction. During data extraction, IDP uses advanced OCR and NLP to recognize and parse data from tables, headers, and free text—even when these elements are misaligned, skewed, or handwritten.
For instance, in a scanned EDS file containing a line like “Motor: TEFC, 440V, 60Hz, 1800 RPM, Frame 286T,” the IDP engine can identify this line as a description of an electric motor, extract voltage, frequency, speed, and frame size, and assign them to predefined fields in the asset master data schema. Similarly, a multi-page Work Order detailing “Pump maintenance on Unit 4B – bearing replacement and shaft alignment,” along with labor hours and spare part usage, is parsed, and the key elements—equipment ID, maintenance task, materials consumed, duration—are structured and pushed into the maintenance management system.
IDP systems capture data from scanned hard copies of EDS, BOMs, work orders, and technical manuals. Documents are digitized into PDFs or images, enabling accurate extraction from real-world, unstructured sources.
Once ingested, machine learning models classify each document based on layout, structure, and key identifiers—determining whether it's an Engineering Design Specification (EDS), Bill of Materials (BOM), Work Order, Technical Manual, etc. This step ensures documents follow the correct extraction path.
For scanned or image-based files, preprocessing steps like de-skewing, noise reduction, contrast enhancement, and orientation correction are applied to improve OCR accuracy.
Advanced OCR engines convert the visual content of the document into machine-readable text. This is especially useful for handwritten annotations on work orders or older scanned EDS files.
NLP models analyze the content to detect and extract structured data. In a BOM, for instance, the system can extract part numbers, materials, quantities, and specifications—even from complex tabular layouts. In an EDS, fields like voltage, RPM, frame size, and enclosure type are identified and parsed into key-value pairs.
Example:
From a line like:"Motor: TEFC, 440V, 60Hz, 1800 RPM, Frame 286T"
IDP extracts:
- Enclosure: TEFC
- Voltage: 440V
- Frequency: 60Hz
- Speed: 1800 RPM
- Frame: 286T
IDP tools use semantic understanding to relate extracted values to their appropriate entities. For example, linking a maintenance task in a Work Order to the corresponding equipment tag or matching part descriptions to internal material codes.
Extracted data is validated against business rules or master data (e.g., valid material codes or equipment IDs). Fields with low confidence scores may be flagged for human review through an assisted validation interface.
Cleaned and validated data is converted into structured formats such as XML, JSON, or CSV and integrated into downstream systems—like a CMMS, ERP, or MDM platform.
Example:
A parsed Work Order might yield:
-Equipment ID: PUMP_4B
-Task: Bearing replacement, Shaft alignment
-Spare Parts: BRG123, ALGN456
-Labor Hours: 6
-Status: Closed
This data is directly fed into a maintenance planning or analytics module.
Corrections made by users are fed back into the model for continuous training, enabling improved accuracy over time for recurring document types and formats.
Traditional IDP solutions rely on static templates or rule-based extraction and often require manual correction of low-confidence fields. Automated IDP adds a layer of intelligence—utilizing self-learning models, business rule validation, and adaptive extraction logic to minimize human involvement. It detects anomalies, auto-corrects formatting issues, and integrates seamlessly with ERP or EAM platforms.
For example, in a BOM extracted from a vendor catalog PDF, automated IDP can parse descriptions like “Valve, Globe, 2”, SS316, 150# RF, Bolted Bonnet” and break it into attributes: valve type, size, material, pressure rating, and connection type—then match it to existing material codes, flag duplicates, or suggest standardized descriptions based on the organization’s naming conventions. This end-to-end automation drastically reduces engineering cycle times and inventory errors.
Verdantis’ Automated Intelligent Document Processing Software is purpose-built to solve a long-standing challenge in industrial asset-heavy environments: the extraction, validation, and structuring of Bill of Materials (BOM) data from engineering drawings and related documents. The system automates the end-to-end process of interpreting 2D/3D designs, managing BOM versions, and updating them within enterprise data ecosystems—reducing manual effort, error rates, and engineering-to-operations lead time.
1. User Interface Design:
A user-centric drag-and-drop interface allows engineers, planners, and data stewards to upload multiple BOM files simultaneously—supporting seamless interaction with minimal training.
2. AI Agent for BOM Extraction:
A proprietary AI engine built using a combination of deep learning, NLP, and computer vision enables accurate interpretation of tabular and embedded BOM structures from both 2D and 3D design files (e.g., DWG, STEP, PDF, TIFF). The AI agent identifies parts, quantities, descriptions, and relational hierarchies across assemblies and sub-assemblies.
3. Data Ingestion & Backend Architecture:
Files can be ingested via UI or automatically picked up from predefined locations using API integrations. The backend architecture leverages a scalable microservices framework that processes, stores, and forwards structured BOM data to downstream platforms such as Verdantis Integrity.
BOM and material data can be directly pushed into ERP or CMMS systems such as SAP, Oracle, Maximo, and others. Whether you’re upgrading to S/4HANA or optimizing spare parts inventory, your data is ready when you are.
Users can upload documents or configure the agent to pick files via API, SFTP, or direct integration.
The agent reads engineering drawings or structured documents, extracting BOM lines, part details, and equipment data.
Equipment ID links the drawing to backend systems. The agent enriches BOMs with contextual info, checks for duplicates, and creates new material IDs if needed.
The approved BOM is formatted and sent to your ERP or CMMS system—ensuring structured, standardized data every time.
Intelligent Document Processing (IDP) brings transformative benefits to organizations, especially those dealing with complex, high-volume documents such as Engineering Design Specifications (EDS), Bills of Materials (BOM), and Work Orders. By leveraging AI technologies like machine learning (ML), natural language processing (NLP), and optical character recognition (OCR), IDP automates the extraction, classification, and processing of unstructured or semi-structured data. Below are the key benefits of implementing IDP in a business:
Automation of Repetitive Tasks: IDP automates time-consuming tasks such as manual data entry, document sorting, and categorization. For instance, in BOM management, instead of manually extracting and entering part numbers or descriptions, the AI quickly processes complex drawings or documents.
Faster Decision Making: With document automation are processed and analyzed in real time, allowing quicker access to key information. This reduces the lead time for decision-making, especially when handling operational data like maintenance work orders and parts requisitions.
Example:
In the case of maintenance work orders, AI quickly extracts task details (e.g., parts used, labor hours, equipment status), feeding this data into the maintenance management system without human intervention.
Labor Cost Savings: By automating document processing, businesses reduce the need for manual data entry, validation, and document sorting, cutting down on labor costs associated with these processes.
Error Minimization: The reduced need for human input minimizes the chances of errors, thus avoiding costly mistakes such as misclassifying BOM components or entering wrong part numbers in an ERP system.
Example:
Instead of manual data validation in the BOM process, IDP ensures that only valid and consistent data is entered into the system, reducing the chances of rework or material ordering mistakes.
Elimination of Human Error: By using AI models for data extraction, IDP ensures that even complex or messy data (e.g., handwritten notes, scanned drawings) is correctly processed and entered into the system, reducing inaccuracies that commonly occur in manual workflows.
Advanced OCR and NLP: IDP tools use state-of-the-art Optical Character Recognition (OCR) and Natural Language Processing (NLP) to understand and extract data from non-structured formats, ensuring that all relevant information is captured with precision.
Example:
When processing scanned EDS documents, IDP can recognize key technical specifications (e.g., motor voltage, RPM) despite skewed or handwritten text, improving data integrity.
Audit Trails and Version Control: IDP tools come with built-in version control, logging, and auditing capabilities, ensuring that all document revisions (e.g., updated BOMs or technical manuals) are tracked. This is critical for compliance in regulated industries.
Data Integrity: IDP ensures that data entered into the system is consistent with governance rules, such as checking for duplicate materials or confirming that extracted BOM data adheres to master data standards.
Example:
In BOM version control, IDP not only captures the latest changes but also ensures that any modifications are properly approved and recorded, which is essential for regulatory adherence.
Handling Large Volumes of Documents: IDP solutions are designed to scale with growing document volumes. Whether processing hundreds of work orders, BOMs, or technical manuals, the system can efficiently handle high throughput without the need for proportional increases in human resources.
Flexible Integration: IDP tools easily integrate with existing enterprise systems such as ERP, CMMS, or MDM platforms, allowing seamless synchronization of extracted data.
Example:
A global manufacturing company can implement IDP to process BOMs from various design files in multiple formats (e.g., 2D CAD, PDF) without disrupting the current workflow in their enterprise systems.
Focus on Higher-Value Tasks: By automating routine tasks, IDP frees up employees to focus on more strategic, high-value work, such as troubleshooting complex issues, making informed decisions, or managing exceptions.
Fewer Manual Reviews: IDP reduces the need for extensive manual document review processes. While humans can intervene in cases where the AI has low confidence, the system automatically handles the bulk of the work.
Example:
In the maintenance department, staff can focus on critical tasks like equipment inspections or planning new projects, instead of spending time manually entering work order data.
Data Extraction for Analytics: IDP extracts not only structured data but also valuable insights from unstructured content. These insights can be used to improve operational processes, inventory management, and maintenance forecasting.
Real-Time Analytics: The data extracted through IDP can be immediately made available for reporting and analytics, enabling organizations to identify trends and patterns in maintenance, inventory, or production that were previously hidden in paper-based documents.
Example:
By extracting and analyzing data from maintenance work orders, IDP can identify frequent equipment issues, helping to predict future maintenance needs and reduce downtime.
Centralized Data Repository: IDP consolidates extracted data into a structured and easily accessible format, enabling teams across departments (e.g., engineering, operations, procurement) to collaborate using a shared, up-to-date dataset.
Real-Time Updates: Any updates to documents (such as BOM changes or work order status) are immediately reflected across the enterprise system, promoting real-time collaboration.
Example:
Engineering teams can access updated BOMs in real-time, while procurement can use this information to avoid delays in material ordering and inventory management.
Multi-Source Input Flexibility:
Users can upload files directly via UI or allow the AI agent to ingest documents from shared folders, ERP outputs, or PLM systems via API calls. Bulk uploads are supported to fast-track large engineering data migrations or upgrades.
Support for 2D & 3D Engineering Formats:
The AI engine supports standard engineering drawing formats, including 2D schematics and 3D models, ensuring broad compatibility across industries such as manufacturing, oil & gas, and utilities.
Equipment-Aware BOM Extraction:
Users are required to provide only the Equipment ID. The AI agent automatically maps this to existing metadata such as equipment details and functional location, using existing ERP or CMMS integrations.
BOM Version Control & Approval Workflow:
The platform supports intelligent BOM comparison to detect and highlight changes across versions. Before finalization, the system requests user approval, maintaining an auditable version control trail.
Intelligent Material Creation with Integrity Integration:
Parsed BOMs are passed to Verdantis Integrity for material master validation. The system checks for duplicate parts and creates new material IDs only when no matches exist, maintaining governance and eliminating redundancy.
Currently, updates to 2D/3D drawings of equipment, including BOM updates, are done manually. This process is not only time-consuming but also prone to human error. The reliance on manual methods increases the risk of discrepancies in the system and delays in updating critical equipment data, leading to inefficiencies in maintenance, inventory management, and procurement.
Additionally, a growing number of OEMs are directly sending updated equipment data to their clients (via API or other digital means), yet there is no system in place to automatically extract and process this new data. This creates a significant gap in the ability to quickly and accurately integrate updates into the client’s ERP system.
The root cause of this problem lies in the absence of an automated updating process. While OEMs are providing real-time equipment updates, clients have no mechanism to automatically extract the relevant information, send it for approval, and update the data into their ERP system. This leaves businesses stuck in manual workflows that are slow and error-prone, increasing operational risks.
Verdantis’ AI-driven solution can bridge this gap by automating the entire process. The solution is designed to receive updated equipment data (e.g., from OEMs) in multiple formats, including 2D/3D drawings, BOM updates, and technical manuals, and then:
Data Extraction:
The system automatically extracts key details from these drawings or files, such as equipment IDs, BOM components, material descriptions, part numbers, and functional locations. Using advanced AI models, OCR, and NLP, the system can process both structured and unstructured data from different file formats and sources.
Approval Workflow:
The AI system then sends the extracted data for internal approval, ensuring that any changes (such as BOM updates or equipment modifications) are validated before they are integrated into the system. The approval workflow is streamlined, allowing relevant stakeholders to quickly review and confirm changes.
ERP System Integration:
Once approved, the updated data is automatically integrated into the client’s ERP system, such as SAP or Oracle. This can include BOM updates, material master changes, and equipment metadata, ensuring that all data in the ERP is accurate, up-to-date, and consistent across the organization.
Speed & Efficiency:
With automated updates, the time spent on manual data entry and review is drastically reduced. This speeds up the entire process, allowing quicker decision-making and preventing costly delays in maintenance and production.
Reduced Errors:
By automating the data extraction and approval process, the risk of human error is minimized. This ensures that the data entering the ERP system is accurate, reducing discrepancies in materials, equipment, and work orders.
Seamless Integration:
The AI-driven solution seamlessly integrates with the existing ERP system, allowing businesses to adopt the technology without disrupting their current workflows.
Cost Savings:
Automating the BOM and equipment update process reduces labor costs and minimizes the need for manual oversight, resulting in cost savings over time.
Scalability:
The system can handle large volumes of updates from multiple OEMs and manage data from complex equipment and BOM structures, making it scalable for enterprises with vast inventories or large-scale operations.
In a manufacturing environment, an OEM sends an updated BOM for a pump assembly via API, including part numbers, new materials, and design changes. The AI-driven IDP solution extracts the relevant data from the updated BOM, compares it against existing records in the client’s ERP system, and sends it for approval. Once validated, the updated BOM is automatically integrated into the ERP system, ensuring that procurement, maintenance, and inventory management teams work with the most current data.
This AI-driven approach not only saves time but also ensures that critical equipment data is always up-to-date, enabling better decision-making, improved maintenance schedules, and more efficient resource allocation.
In an era where operational efficiency and accuracy are paramount, Intelligent Document Processing (IDP) emerges as a game-changer, particularly for industries dealing with complex documents like BOMs, work orders, and technical manuals. By automating the extraction, validation, and integration of data, IDP not only saves time but also ensures that your systems are always updated with the most accurate and relevant information.
For organizations handling large-scale equipment data, such as BOM updates or maintenance work orders, implementing an AI-driven IDP solution streamlines workflows, reduces errors, and improves decision-making. The result is a more efficient, cost-effective operation with greater data integrity and reduced risk.
As the demand for real-time, error-free updates grows, IDP solutions like the one provided by Verdantis are transforming the way businesses manage their critical data, enabling them to stay competitive, reduce downtime, and maximize productivity. The future of data processing is automated, intelligent, and seamless — and those who embrace this transformation will be better positioned for long-term success.
By combining document automation with intelligent document processing software, your teams can reduce manual effort, eliminate data inconsistencies, and accelerate decision-making—leading to better inventory planning, procurement accuracy, and digital transformation readiness.
The Document Extraction Agent works in harmony with enterprise platforms, enabling clean, enriched data to flow directly into:
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