Benefits of intelligent document processing.
By automating document processing, IDP reduces the time and effort required to locate, validate, and input data for business processes, allowing employees to focus on higher-value work.
Eliminate process bottlenecks
Unblock enterprise workflows and eliminate delays by automating document data extraction, empowering teams to move faster, adapt quickly, and focus on high-value decisions.
Make every decision count
Delivers accurate, validated, and enriched data directly into your systems and processes. With complete context at their fingertips, decision-makers act with confidence, improving business outcomes and reducing costly errors.
Protect your business
Ensure compliance and safeguard sensitive information with automated processing and full audit trails, minimizing risk while freeing employees from manual checks.
Simplify change, maximize ROI
Deploy faster and reduce complexity with generative AI for extraction. Lower setup costs, accelerate time-to-value, and make transformation initiatives easier for your teams.
Scale without limits
Expand from thousands to millions of documents without sacrificing accuracy. Adapt instantly to countless document variations, keeping businesses agile and resilient.
How does intelligent document processing work?
Data is at the heart of digital transformation, yet most business data is inaccessible, embedded in documents, emails, images, and PDFs. AI document processing makes any and all data accessible for business processing by converting unstructured and semi-structured documents into usable information to fuel automating document-centric business processes. IDP uses AI technologies such as natural language processing (NLP), computer vision, machine learning (ML), and generative AI to classify, categorize, and extract relevant information, as well as validate the extracted data. IDP tools are completely non-invasive, integration-friendly, and work seamlessly with agentic process automation to power digital operations.
Document Input
Documents are first ingested from multiple channels, including mobile devices, email, shared folders, network scanners, and direct connections to business systems via API or pre-built connectors. This flexibility empowers users to efficiently support diverse business processes, adapting to your specific needs and streamlining operations from every entry point.
Image Pre-Processing
The second step in intelligent document processing is pre-processing. This step involves binarization, noise reduction, de-skewing, and de-speckling. These techniques help to improve the quality of the document images before they are processed by OCR and AI models. This ensures that the data extracted is as accurate as possible, minimizing errors in downstream processes.
OCR
The next step uses AI-based optical character recognition (OCR) and intelligent character recognition (ICR) technologies to digitize printed and handwritten text, preparing it for further processing. These technologies are able to recognize the logical structure of the whole document, including complex elements such as tables, enabling document classification, data extraction, and high-quality export to digital formats.
Document Classification and Assembly
The next step is intelligent document classification and assembly. This step involves analyzing both the text and image features, unsupervised and supervised learning, through multi-modal learning. This allows for more efficient routing of documents to the appropriate processing workflows. By incorporating human-in-the-loop input, classification models learn from user corrections and automatically adjust, continuously improving over time.
Data Extraction and Validation
The fourth step is data extraction from structured, semi-structured, and unstructured business documents, where AI and machine learning are used to extract relevant data in more than 200 languages. This can include text, complex tables, handwriting, barcodes, and even signatures. Data extraction uses pre-trained models for specific documents, custom-trained models, and large language models (LLMs) and GenAI for extraction.
Domain-specific validation cross-checks information against databases and systems, applying fuzzy logic, regular expression (RegEx), rules, and scripts to assess, match, and manage the extracted data for accuracy and relevance to the specific industry or business context. Additionally, enhanced validation within an agentic process automation workflow can further verify the extracted data for suitability to the prescribed purpose or process.
Human-in-the-Loop and Continuous Learning
Human-in-the-loop (HITL) validation is another component of IDP that lets business users step in to the manual check and increases the quality of automated data processing. This optional step provides a rapid feedback loop and fine-tune AI training models by correcting data via human input.
Data Integration
The final step is exporting the extracted data in the required format, such as JSON, CSV, or XML, and passing it to agents for next-best action. The data is then sent seamlessly to your automation systems and business applications through simple REST API or pre-built connectors. Agents are able to take action on the data to make decisions.
Leveraging IDP with agentic process automation
Automation can only go so far as the data it has to work with. In modern automation environments, data processed by IDP often becomes part of a broader workflow, where it is used to inform decisions and trigger next steps.
Effectively extracting and structuring information is the gateway to automating the large share of business processes that still rely on manual inputs today. When IDP is embedded into a comprehensive agentic automation environment, organizations can move beyond isolated document extraction and begin automating processes end-to-end, with data extraction and downstream actions operating in sync.
Get from setup to production faster
In agentic process automation environments, IDP can be deployed significantly faster than traditional document processing systems, allowing AI-driven workflows to start using document data almost immediately.
Turn documents into orchestrated workflows
Extracted document data flows directly into coordinated workflows where AI agents, automation tools, systems, and people each play a role in moving work forward.
Scale without adding manual work
As AI agents observe outcomes and incorporate human feedback, document-driven processes become more reliable, require fewer manual touchpoints, and maintain consistent performance even as volumes and formats grow.
Process data in context
Rather than isolating document handling in a separate step, IDP can surface extracted data inside the applications, workflows, and conversational AI where decisions are made, so users review and correct information as part of the process itself.
Adapt workflows without rework
Agentic automation benefits from IDP configurations that are easy to adjust as workflows evolve, allowing teams to refine document types, rules, and validation logic without reengineering entire processes.
Handle any document
AI agents depend on consistent access to structured information. Modern IDP makes this possible even when inputs range from standardized forms to loosely structured emails and free-form documents.
Protect sensitive data and maintain compliance
When document data becomes part of automated decision-making, consistent handling and traceability matter. IDP helps ensure sensitive information is processed accurately and in ways that support audit and compliance needs.
Improve results over time
In agentic systems, document processing improves alongside the workflows it supports. As agents learn from new scenarios and human input, the quality and usefulness of extracted data increase over time.
Connect document data across your systems
To support agent-driven processes, IDP must connect easily to other systems, models, and automation tools, so document data can be reused wherever decisions and actions occur.
IDP application use cases across industries
Intelligent document processing software is ready to extract and organize data across industries and business functions, right out of the box.
Banking and Finance
Automate know your customer (KYC), loan packets, account servicing documents, and financial statements to deliver a smarter, faster banking experience.
Healthcare
Digitize patient records, onboard patients, process insurance claims (EOB), and extract data from medical forms to improve accuracy and speed up the billing process.
Insurance
Automate property and casualty claims processing and policy issuance, extract data from policy documents, and improve fraud detection.
Manufacturing
Automate document-heavy processes from supply chain management to invoice processing to quality control documents.
Logistics
Fast-track your supply chain by automating data extraction from bills of lading, customs declarations, and commercial invoices, speeding up delivery and reducing errors.
Accounting and Finance
Unlock efficiency and reduce costs by streamlining back-office workflows for common documents like invoices and purchase orders, expense reports, and other financial documents.
Human Resources
Accelerate employee onboarding processes, manage resumes and job applications, and extract data from HR forms.
The evolution of intelligent document processing.
Data entry
Document processing has long been a labor-intensive and time-consuming task for organizations. Data entry represented a full-time effort in and of itself. For decades, legacy capture systems built on optical character recognition (OCR) provided the only data extraction solution, enabling partial automation of data capture by converting images into text. OCR solutions applied templates and extraction rules to map extracted text into a usable structured format.
OCR made easy
With the rise of computing and digital documents, the amount of business data increased astronomically. Initial document processing solutions provided user-friendly interfaces atop OCR functionality. This added accessibility, making it easier to connect OCR output with desired data fields, but still the setup time and maintenance required a deep technical skill set.
Enter IDP
Intelligent document processing gets its name from the AI technologies that power its data extraction and transformation capabilities, extending automation beyond structured and semi-structured documents to unstructured information. At the core of most IDP solutions are machine learning (ML) models that address a specific range of use cases, such as invoices or mortgage documents, enabling high-accuracy data extraction and processing but requiring extensive training.
IDP supercharged with large language models (LLMs) and generative AI
Where traditional document processing tools relied on templates, rules, and even machine learning models that needed lots of labeled data, large language models (LLMs) and generative AI have transformed processing by introducing flexibility, intelligence, and scalability.
The emergence of generative AI for extraction has opened a new world of possibilities for handling complex documents — long form, completely unstructured — with no need for extensive training. The use of LLMs inside a turnkey IDP solution that provides the added contextual layer of understanding documents and data which provides the added boost to accuracy.
IDP as part of agentic process automation
In the agentic era, AI agents are joining with IDP to power faster decision making as part of document workflows. Data that is intelligently extracted using ML models or generative AI is sent to AI agents that use an understanding of the content and reasoning to validate and reconcile data, and make decisions.
The future of document automation is happening now with agentic systems that not only process data but turn it into action — driving speed, accuracy, and strategic value across the enterprise.
Put your data to work with agentic process automation
Document Automation is built into the Agentic Process Automation System to seamlessly bring document data into any process and accelerate end-to-end automation, by combining AI-powered intelligent document processing with seamless process orchestration.
Quickly extract and validate data, then connect it to agents, actions, and systems — all in one unified solution designed for speed, accuracy, and scalability.
Build vs Buy.
Building an IDP solution gives you full control and customization. But it also comes with significant challenges that can slow you down and increase costs. In a build your own IDP system, each component will need to be built from scratch or sourced from multiple third-party providers.
Here are the major risks to keep in mind:
Cost, time, and resources
Building a custom IDP solution isn’t just about writing code, it’s a major investment. You’ll need:
- Specialized talent in software development and data science (often expensive and hard to find)
- Ongoing maintenance for updates, new features, and scaling, pulling your best people away from other priorities
- Hidden costs like infrastructure and unpredictable licensing fees
And then there’s time. Developing all the components including workflow orchestration, data processing, dashboards can take months. Compare that to a turnkey platform where these capabilities are already built-in and ready to deliver value immediately.
Complexity to setup and maintain
Owning every part of the solution sounds great, until you realize what that means:
- Building and maintaining document models requires deep expertise, even with LLMs and prompt engineering
- Scaling to hundreds of document use cases quickly becomes overwhelming
- Integration with other systems and human-in-the-loop (HITL) experience adds another layer of complexity
A ready-made platform solves these challenges with pre-built models, seamless integrations, optimized user experiences, and full end-to-end process orchestration so your team can focus on innovation, not infrastructure an development.
Risk of falling short
Custom solutions often struggle to meet business goals because:
- LLM foundational models work best when grounded with accurate, business-contextual understanding from business documents. By themselves, LLMs can be unpredictable or miss important details that impact accuracy.
- Measuring performance and ROI is difficult without built-in learning model evaluation and monitoring tools that help guide users to fine tuning learning instances.
Turnkey IDP solutions come with an added layer of contextual understanding of data, continuous learning, and built-in analytics, helping you hit your accuracy targets and maximize ROI.
Lack of governance controls
Custom builds rarely include robust governance controls out of the box capabilities and will require custom tools to meet the security requirements of a company. That means:
- Sensitive data may not be fully protected. Consider the types of documents and data being processed, and the data will be protected at each step within the process.
- Traceability and responsible AI usage are hard to enforce. The ability to audit and monitor document processes, tool usage, and performance trends is important to detecting drift or anomalies in the results.
- Scaling compliance across hundreds of document processes becomes a major challenge. It’s important to have tools to benchmark agents and models for accuracy, consistency, and performance before they scale.
Buying an IDP solution gives you enterprise-grade security, compliance, and governance from day one, reducing risk and giving peace of mind.
Frequently asked questions.
What is the difference between OCR and intelligent document processing?
Optical character recognition (OCR) is just one component of IDP. OCR is a technology that recognizes and converts printed or handwritten text into digital form, while intelligent document processing (IDP) involves a more advanced process that not only extracts data but also classifies, validates, and integrates it for use in business processes. OCR alone cannot provide the same level of accuracy and efficiency as the combination of technologies used in IDP. Additionally, IDP offers scalability and automation, making it more suitable for handling large volumes of documents in various industries.
How accurate is intelligent document processing?
Intelligent document processing is known for its high level of accuracy, with solutions attaining up to 99% accuracy rates. This is due to the use of multiple advanced technologies, such as NLP, OCR, large language models (LLM), generative AI, and machine learning, which work together to extract and validate data from documents. Additionally, human-in-the-loop validation can further improve the accuracy by allowing for feedback and corrections from human input. Overall, intelligent document processing offers a highly accurate solution for managing large volumes of documents with complex data.
How does intelligent document processing improve data accuracy and efficiency?
As an AI-driven technology, intelligent document processing software continuously learns as it processes data to improve accuracy and efficiency over time. In addition, human-in-the-loop validation can further boost accuracy by allowing for human review and input.
Can intelligent document processing handle handwritten text?
Yes, intelligent document processing can handle handwritten text by employing intelligent character recognition (ICR) technology to decipher hard-to-read text.
What functional areas within enterprises benefit most from IDP?
Intelligent document processing has wide-reaching benefits across functional areas within enterprises. Some of the functional areas that can benefit the most from IDP include finance, legal, and HR by automating document-based workflows and improving efficiency. Any function that deals with large volumes of documents and complex data is an ideal candidate for streamlining processes through IDP.
How does IDP work with agentic process automation (APA)?
IDP extracts and validates document data, and then hands it to AI agents for reasoning, decisioning, and action. By connecting information to AI agents as part of the Agentic Process Automation System, AI agents can better analyze the data, suggest actions, and even act autonomously to perform deterministic and cognitive tasks, delivering greater efficiency and results.
