Moresco’ AI-powered Intelligent Document Processing (IDP) solution automates the extraction and integration of data from complex documents such as BOMs, work orders, and technical manuals. With support for various 2D and 3D file formats and seamless ERP integration, the platform delivers accurate, real-time updates while reducing manual effort and minimizing errors.
Unstructured documents remain a major hurdle for many organizations. Items like BOMs, work orders, technical schematics, and equipment manuals still contain more than 80% of essential business data in formats that traditional systems struggle to process. Intelligent Document Processing (IDP) addresses this gap by converting unstructured files into structured, usable information.
Unlike standard OCR tools that simply convert images into text, IDP combines Artificial Intelligence (AI), Machine Learning (ML), Natural Language Processing (NLP), and Robotic Process Automation (RPA). These technologies work in tandem to extract, interpret, and validate information within its business context. The focus goes beyond automation—IDP delivers intelligence by identifying patterns and extracting insights that lead to smarter decision-making and streamlined operations.
Intelligent Document Processing (IDP) is an advanced technology that integrates Artificial Intelligence (AI), Optical Character Recognition (OCR), Machine Learning (ML), and Natural Language Processing (NLP) to extract structured data from complex, unstructured documents. Unlike conventional OCR or manual data entry methods, IDP goes beyond surface-level text capture—it understands document layout, context, and meaning to streamline and automate content-rich business processes.
In asset-intensive sectors, critical data is often buried in technical documents like Engineering Design Specifications (EDS), Bills of Materials (BOMs), Work Orders, and Maintenance Manuals. These documents typically exist as PDFs or scanned files, have inconsistent formats, and use industry-specific language. Traditional automation tools fall short in processing such data effectively. IDP platforms trained on domain-relevant models can accurately extract valuable information such as part numbers, specifications, tolerances, supplier references, and maintenance instructions—empowering smarter operations and improved data accessibility.
The process begins by capturing documents from diverse physical formats—such as Equipment Data Sheets (EDS), Bills of Materials (BOMs), Work Orders, and technical manuals. These are scanned and digitized before being fed into the system. Using intelligent classification models, the system identifies the type of document (e.g., BOM vs. EDS) and determines the appropriate extraction workflow. Advanced OCR and NLP algorithms then extract data from varied layouts—tables, headings, and even free-form or handwritten content—despite challenges like skewed or poorly aligned inputs.
For example, a scanned EDS entry reading “Motor: TEFC, 440V, 60Hz, 1800 RPM, Frame 286T” would be understood by the IDP engine as motor-related data. It extracts key attributes such as voltage, frequency, speed, and frame size, then maps them to corresponding fields in the asset master schema. Similarly, a lengthy Work Order describing tasks like “Pump maintenance on Unit 4B – bearing replacement and shaft alignment”, including resource usage and labor hours, is automatically broken down into structured data—covering equipment ID, task type, materials used, and time spent—and integrated directly into the maintenance management system.
IDP systems begin by collecting information from scanned physical documents such as EDS, BOMs, work orders, and technical manuals. These documents are converted into digital formats like PDFs or images, making it possible to accurately extract valuable data from unstructured, real-world sources.
After ingestion, advanced machine learning algorithms categorize each document by analyzing its layout, structure, and key markers—identifying whether it’s an Engineering Design Specification (EDS), Bill of Materials (BOM), Work Order, Technical Manual, or other types. This classification step ensures documents are routed correctly for precise data extraction.
Scanned and image-based documents undergo preprocessing steps such as de-skewing, noise removal, contrast adjustment, and orientation correction to enhance image quality and boost OCR accuracy.
Cutting-edge OCR technology transforms the visual elements of documents into editable, machine-readable text. This capability is particularly valuable for interpreting handwritten notes on work orders or digitizing older scanned Engineering Design Specifications (EDS).
Natural Language Processing (NLP) models analyze document content to identify and extract structured data. For example, in a Bill of Materials (BOM), the system can pull out part numbers, materials, quantities, and specifications—even from complex tables. In an Engineering Design Specification (EDS), it identifies fields like voltage, RPM, frame size, and enclosure type, converting them into key-value pairs.
Example:
From a line
like:"Motor: TEFC, 440V, 60Hz, 1800 RPM,
Frame 286T"
IDP extracts:
Intelligent Document Processing (IDP) leverages semantic analysis to accurately connect extracted data with their relevant entities. For instance, it links a maintenance task in a Work Order to the correct equipment tag or associates part descriptions with internal material codes, ensuring meaningful and actionable relationships.
Extracted information is cross-checked against business rules and master data—such as approved material codes or equipment identifiers. Data entries with low confidence levels are flagged for review via an assisted validation interface, ensuring accuracy before final approval.
Validated and refined data is transformed into structured formats like XML, JSON, or CSV, then seamlessly integrated into downstream platforms such as CMMS, ERP, or MDM systems.
Example:
A processed
Work Order might include:
- Equipment ID: PUMP_4B
- Task: Bearing replacement, Shaft alignment
- Spare Parts: BRG123, ALGN456
- Labor Hours: 6
- Status: Closed
This structured data is then directly fed into maintenance planning or analytics systems for immediate use.
User corrections are incorporated back into the system, allowing the model to continuously learn and enhance accuracy for recurring document types and formats over time.
Traditional IDP solutions typically rely on fixed templates or rule-based extraction methods and often require manual review and correction for fields with low confidence. In contrast, Automated IDP incorporates advanced intelligence through self-learning models, business rule validations, and adaptive extraction techniques designed to reduce human intervention. It can detect anomalies, automatically correct formatting inconsistencies, and integrate smoothly with ERP or EAM systems.
For example, when extracting a BOM from a vendor catalog PDF, automated IDP can parse a description such as “Valve, Globe, 2”, SS316, 150# RF, Bolted Bonnet” and decompose it into detailed attributes like valve type, size, material, pressure rating, and connection type. The system can then match this data to existing material codes, flag duplicates, or recommend standardized naming conventions based on organizational guidelines. This level of end-to-end automation significantly reduces engineering cycle times and minimizes inventory errors.
Moresco’ Automated Intelligent Document Processing Software is specifically designed to address a persistent challenge in asset-intensive industries: extracting, validating, and structuring Bill of Materials (BOM) data from engineering drawings and related documentation. The solution streamlines the entire workflow—interpreting both 2D and 3D designs, managing multiple BOM revisions, and seamlessly updating enterprise data systems—significantly cutting down manual workload, minimizing errors, and accelerating the engineering-to-operations timeline.
1. User Interface Design:
A
user-friendly drag-and-drop interface enables engineers,
planners, and data stewards to upload multiple BOM files at
once, promoting easy and intuitive use with minimal training
required.
2. AI Agent for BOM Extraction:
Powered by a proprietary AI engine that combines deep
learning, natural language processing, and computer vision,
this component precisely interprets complex tabular data and
embedded BOM structures from 2D and 3D design formats (such
as DWG, STEP, PDF, TIFF). It efficiently extracts part
numbers, quantities, descriptions, and the hierarchical
relationships among assemblies and sub-assemblies.
3. Data Ingestion & Backend Architecture:
Files can be uploaded manually through the user
interface or automatically retrieved from designated sources
via API integrations. The backend utilizes a scalable
microservices framework to efficiently process, store, and
transmit structured BOM data to downstream systems such as
Moresco Assure.
BOM and material data can be seamlessly integrated and pushed directly into ERP or CMMS systems such as SAP, Oracle, Maximo, and others. Whether you’re upgrading to S/4HANA or optimizing spare parts inventory management, your data will be ready when you are.
Users can upload documents directly or configure the system to ingest files via API, SFTP, or other direct integrations.
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:
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:
While processing scanned EDS documents, IDP accurately identifies critical technical details—such as motor voltage and RPM—even when the text is skewed or handwritten, thereby enhancing data accuracy.
Audit Trails and Version Control: IDP solutions feature integrated version control, logging, and auditing functionalities to track all document updates (such as revised BOMs or technical manuals). This capability is essential for meeting compliance requirements in regulated industries.
Data Assure: IDP guarantees that data entered aligns with governance policies by detecting duplicate materials and ensuring that extracted BOM information complies with master data standards.
Example:
In BOM version control, IDP not only records the most recent updates but also guarantees that all changes are formally approved and documented, which is crucial for regulatory compliance.
Managing High Document Volumes: IDP solutions are built to scale effortlessly as document volumes increase. Whether dealing with hundreds of work orders, bills of materials (BOMs), or technical manuals, these systems can process large quantities efficiently without requiring a proportional rise in human labor.
Seamless Integration: IDP tools can be easily integrated with existing enterprise systems like ERP, CMMS, or MDM platforms, enabling smooth synchronization of the extracted information.
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.
Shift to High-Impact Tasks: Automating routine processes with IDP allows employees to concentrate on more meaningful and strategic activities, such as resolving complex problems, making data-driven decisions, and handling exceptions.
Minimized Manual Effort: IDP significantly reduces the need for time-consuming manual document reviews. While human input is still valuable for low-confidence cases, the system handles the majority of the workload automatically.
Example:
In the maintenance department, employees can prioritize essential tasks such as conducting equipment inspections or planning upcoming projects, rather than spending time manually inputting work order information.
Insightful Data Extraction: IDP captures both structured and unstructured data, uncovering valuable insights. These insights can help optimize operations, improve inventory control, and enhance maintenance planning.
Instant Access to Analytics: The data gathered through IDP is instantly available for analysis and reporting, allowing organizations to spot trends and patterns in areas like maintenance, inventory, and production—insights that would otherwise remain buried in paper records.
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.
Unified Data Access: IDP organizes extracted information into a centralized, structured format, making it easily accessible. This allows cross-functional teams—such as engineering, operations, and procurement—to collaborate effectively using a consistent and current dataset.
Live Document Updates: Changes to documents, like updates to BOMs or work order statuses, are instantly synchronized across systems. This ensures teams are always working with the most up-to-date information, enhancing 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.
Flexible Multi-Source Input:
Users can upload files through the UI or let the AI agent
automatically ingest documents from shared folders, ERP
exports, or PLM systems via API. Bulk upload support
accelerates large-scale engineering data migrations or
upgrades.
Compatibility with 2D & 3D Engineering Formats:
The AI engine supports widely used engineering formats,
including 2D schematics and 3D models, ensuring seamless
integration across industries like manufacturing, oil &
gas, and utilities.
Context-Aware BOM Extraction:
By simply providing an Equipment ID, users enable the AI
to fetch related metadata such as equipment details and
functional location, leveraging existing ERP or CMMS
integrations.
BOM Version Tracking & Approval:
The platform automatically detects changes between BOM
versions and prompts users for approval before finalizing,
ensuring traceable and auditable version control.
Smart Material Creation with Assure
Integration:
Parsed BOM data is routed to
Moresco Assure for material master
validation. The system checks for duplicates and generates
new material IDs only when necessary, upholding data
governance and preventing redundancy.
Updating equipment data—such as 2D/3D drawings and BOMs—is currently a manual process. This approach is not only time-intensive but also susceptible to human error. These inefficiencies lead to delays in maintaining accurate equipment records, which in turn affects maintenance planning, inventory accuracy, and procurement operations.
Compounding the issue, more OEMs are delivering updated equipment data directly to clients through APIs and other digital channels. However, most organizations lack a system to automatically capture and process this incoming data. As a result, there's a significant gap in the ability to quickly and reliably integrate these updates into ERP systems.
The core issue stems from the lack of an automated update process. Although OEMs provide real-time equipment updates, clients currently have no system to automatically extract, approve, and integrate this data into their ERP systems. As a result, businesses remain reliant on slow, manual workflows prone to errors and operational risks.
Moresco’ AI-driven solution addresses this challenge by fully automating the process. It is designed to handle updated equipment data from various sources, including OEMs, in multiple formats such as 2D/3D drawings, BOM updates, and technical manuals. The solution then:
Data Extraction:
The system automatically extracts essential information
from these files, including equipment IDs, BOM
components, material descriptions, part numbers, and
functional locations. Leveraging advanced
AI models, OCR, and
NLP, it processes both structured and
unstructured data across diverse file types and sources.
Approval Workflow:
Extracted data is routed through an internal approval
process, ensuring that any modifications—such as BOM or
equipment changes—are verified before integration. This
streamlined workflow enables stakeholders to quickly
review and approve updates.
ERP System Integration:
Upon approval, updated information is seamlessly
integrated into the client’s
ERP system (e.g., SAP, Oracle). This
includes BOM updates,
material master revisions, and
equipment metadata, guaranteeing that
ERP data remains accurate, current, and consistent
throughout the organization.
Speed & Efficiency:
Automation significantly reduces the time spent on
manual data entry and review, accelerating processes and
enabling faster decision-making while avoiding costly
delays in maintenance and production.
Reduced Errors:
Automating data extraction and approval minimizes human
errors, ensuring that only accurate and consistent data
is entered into the ERP system, reducing discrepancies
in materials, equipment, and work orders.
Seamless Integration:
The AI-powered solution integrates smoothly with
existing ERP platforms, allowing businesses to adopt it
without disrupting their current workflows.
Cost Savings:
By automating BOM and equipment updates, labor costs are
lowered and manual oversight is minimized, resulting in
long-term cost efficiencies.
Scalability:
The system can efficiently process large volumes of
updates from multiple OEMs and handle complex equipment
and BOM data, making it ideal for enterprises with
extensive inventories and large-scale operations.
In a manufacturing setting, an OEM provides an updated BOM for a pump assembly via API, including part numbers, new materials, and design revisions. The AI-powered IDP solution extracts the key data from this updated BOM, compares it with existing information in the client’s ERP system, and routes it for approval. Upon validation, the updated BOM is automatically integrated into the ERP, ensuring procurement, maintenance, and inventory teams always access the latest data.
This AI-driven process not only accelerates updates but also guarantees that critical equipment information remains current, facilitating better decision-making, optimized maintenance schedules, and more efficient allocation of resources.
In today’s landscape where operational efficiency and precision are crucial, Intelligent Document Processing (IDP) stands out as a transformative solution, especially for industries managing complex documents such as BOMs, work orders, and technical manuals. By automating data extraction, validation, and integration, IDP not only saves valuable time but also guarantees your systems remain updated with the most accurate and relevant information.
For organizations dealing with extensive equipment data—like BOM revisions or maintenance work orders—deploying an AI-powered IDP solution optimizes workflows, minimizes errors, and enhances decision-making. This leads to a more streamlined, cost-effective operation with improved data Assure and lower risks.
As the need for timely, error-free updates intensifies, IDP solutions such as those offered by Moresco are revolutionizing how companies handle critical data, helping them stay competitive, reduce downtime, and boost productivity. The future of data management is automated, intelligent, and seamless — and adopting these technologies is key to sustaining long-term success.
By combining document automation with intelligent document processing, your teams can significantly reduce manual effort, eliminate data inconsistencies, and accelerate decision-making—resulting in improved inventory planning, procurement accuracy, and readiness for digital transformation.
The Document Extraction Agent seamlessly integrates with enterprise platforms, enabling clean, enriched data to flow directly into:
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Unit 508, 5th Floor, Active Business Park, 54/10,
Debendra Chandra Dey Rd, Tangra, Kolkata, West Bengal 700046
Unit 508, 5th Floor, Active Business Park, 54/10,
Debendra Chandra Dey Rd, Tangra, Kolkata, West Bengal 700046
Unit 508, 5th Floor, Active Business Park, 54/10,
Debendra Chandra Dey Rd, Tangra, Kolkata, West Bengal 700046
Unit 508, 5th Floor, Active Business Park, 54/10,
Debendra Chandra Dey Rd, Tangra, Kolkata, West Bengal 700046
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