Unlock Unstructured Data with Intelligent Document Processing (IDP)

Intelligent Document Processing (IDP) is AI-driven, automated processing of documents. Enterprises must handle large volumes of semi-structured (e.g., invoices, orders) and unstructured documents (e.g., emails, legal documents, procedures) with greater accuracy and speed. Various estimates indicate that 80% of enterprise data is unstructured. Processing these large quantities of documents manually takes an enormous amount of time.

IDP solutions capture data from documents (e.g., email, text, pdf, and scanned documents), categorize/classify documents, and extract relevant data for further processing using artificial intelligence (AI) technologies such as computer vision, optical character recognition (OCR), Natural Language Processing (NLP), and machine/deep learning (ML/DL).

The benefits for our customers are:

• Increases productivity (operational efficiency, lower costs)
• Reduces processing times
• Reduces data entry
• Reduces labor requirements and optimizes human capital
• Improves accuracy and limits human errors
• Improves customer satisfaction
• Stores and processes documents compliant with GDPR/privacy legislation
• The solution is cloud-native and combines a complete set of AI features with multiple classification and extraction methods, and an intuitive user interface
• Accurate data extraction from best-in-class OCR with 99% accuracy
• Comprehensive Reduction of error-prone manual tasks, up to 90% task reduction

Differentiators compared to other IDP solutions:

• Not only suitable for structured and semi-structured documents but also for unstructured types of documents where context is important to make data meaningful
• Highly accurate OCR-engine embedded (for converting printed or written text from a scanned document or image file into a machine-readable form)
• Single and multi-label classification—user-defined classifiers with easy training for single-value or multi-value output
• Hybrid data extraction: Combining machine learning models with rules/patterns, resulting in faster and optimal results (less training data needed)
• Usability: Focus on the business user and not the data scientist
• Cloud-native solution, scalable services-based architecture, deployable in private, public, and hybrid cloud environments
• Document management (DMS) features embedded: Document store, flexible metadata model, authorization, search, and data source integrations

• ProcessMaker IDP can eliminate most data entry activities within document-centric business processes resulting in increased production and lower handling costs
• Enhance customer satisfaction due to faster processing of requests and decision-making
• Reducing human errors by automation and data validation
• Increase competitive advantage of enterprises (better service, more scalable business)
• Optimize human capital of enterprises - focus on more strategic and customer-centric tasks

• Scanned documents and images containing text
• Native or digital-born documents
• Documents containing tables with data to be captured
• Documents containing barcodes
• Documents containing machine-readable zones like identification documents
• Documents containing name/value pairs to be captured or clauses
• Documents containing text in one of the 120 supported languages
• Documents that contain sensitive information that has to be masked/anonymized

Compared to other solutions, ProcessMaker IDP offers:

• Superior OCR performance with 99% accuracy
• A faster time to value with prepackaged quick-start plugins
• Seamless integration with existing applications
• The complete set of AI tools for accurate data extraction
• A comprehensive set of built-in DMS features
• Outstanding search capabilities

ProcessMaker IDP includes an integrated Document Store with Document Management and document archiving functionality (Document Management System). This offers the advantage of storing and archiving documents according to procedures and legislation. And when the documents require reprocessing due to additional data points, the documents are immediately available and don’t need to be transferred from several sources.

For customers who want to use the DMS features to store documents captured in other systems, we have broad experience in migrating documents, including folder structures and corresponding metadata. In some customer cases, migration can be prevented by leaving the old system in a stand-by modus until the retention period is reached and redirecting the document feeds to our DMS.

Depending on your situation, you can use the built-in DMS or a third-party DMS, or you might be able to skip DMS altogether if you don’t need to save images.

Many customers have a legacy DMS and want to leverage the built-in DMS. PM IDP can help you with this decision and any associated migration to our DMS solution.

ProcessMaker IDP comes with modern APIs that allow you to connect to your existing DMS or other applications. Also, ProcessMaker IDP easily connects with your file shares, mailboxes, and FTP server.

The cloud-native application can also be deployed on-prem or in a hybrid configuration.

It can be deployed on any major cloud architecture, including, but not limited to, Microsoft Azure, AWS, Google, Oracle, and others.

There is a native integration with ProcessMaker, or you can operate it independently.

It will depend on the complexity of the document, the sensitivity or criticality of the application, and the amount of manual effort required.

There is no lower limit for complex documents with many fields and data to extract. The threshold for simpler documents would be >100 pages/daily.

Artificial Intelligence (AI), refers to the simulation of human intelligence in machines/programs that are programmed to think like humans and mimic their actions.

Natural Language Processing (NLP) is a field of AI in which computers, by applying machine learning, analyze, understand, and derive meaning from human language.

Machine Learning (ML) is a subset of AI, which refers to learning from experience, so a computer program can automatically learn from and adapt to new data. Deep learning techniques enable this automatic learning by absorbing huge amounts of unstructured data such as text, images, or video.

ProcessMaker IDP utilizes NLP, statistical machine learning, and deep learning.

Yes, it will reduce manual efforts associated with data & documentation management - classifying/storing, entry, search, etc.

It will also reduce the costs associated with human error.

Beyond cost, IDP allows you to digitize your data, which opens up an array of new opportunities to reduce cost, generate revenue, and service customers.

ProcessMaker IDP is cloud-native and can be deployed on-premise or in a hybrid configuration. It can be deployed on any major cloud architecture, including Microsoft Azure, AWS, Google, Oracle, etc. No special cloud services other than some virtual machines (specs depending on load and required redundancy).

• Multi-factor authentication and advanced authorization
• Encryption of data in transit and at rest
• Regular penetration testing according to a security testing policy

Natural Language Processing (NLP) is a field of AI in which computers, by applying machine learning, analyze, understand, and derive meaning from human language.

Machine Learning (ML) is a subset of AI that refers to learning from experience, so a computer program can automatically learn from and adapt to new data. Deep learning techniques enable this automatic learning by absorbing huge amounts of unstructured data such as text, images, or video.

No, just normal virtual servers. All components are packaged in several containers.

Via the system’s API or the available connectors for email servers, file shares, and FTP sources.

No—a business user can operate and train the system. A more technical user must alter the entity models with additional attributes or build custom value scripts.