Best Intelligent Document Processing (IDP) Software‍

Looking for the right intelligent document processing software? Read this in-depth buyer's guide to find the perfect one for your business.

[Book a Demo\ Take a product tour

\ \ Watch 60s video\ \

Join the

Helping 10,000+ Businesses Streamline Data Processing

.png)

.png)

.png)

.png)

Value you can see and measure

See measurable ROI in weeks, not months

88.3%

Average reduction in manual effort

3.5x

Median ROI over a 6-month payback period

+400K

Hours saved till date and counting

Compare Features Buyers Guide FAQ

BUYERS GUIDE Intelligent document processing software overview

Every day, businesses waste countless hours manually processing invoices, receipts, contracts, and forms. This tedious work not only drains resources but also introduces errors—manual processing typically costs organizations 20 times more than automated alternatives.

‍

Intelligent Document Processing (IDP) is changing this reality. By combining AI technologies—machine learning, computer vision, and natural language processing—IDP automatically extracts, classifies, and validates information from any document type with minimal human intervention. The results are compelling: organizations using IDP reduce processing time by up to 93% and cut operational costs by 62%.

This transformative potential explains why the global IDP market is set to grow from $1.75 billion in 2023 to $19.32 billion by 2032. As more companies move away from manual processing, choosing the right IDP solution becomes crucial. From established names like UiPath and ABBYY to solutions like high-performers like Nanonets, we'll examine the top IDP platforms helping organizations transform their document workflows.

What is Intelligent Document Processing?

Traditional OCR simply converts printed text into digital characters—like turning a scanned page into editable text. Intelligent Document Processing goes far beyond this.

IDP actually understands document content. It identifies key information, recognizes relationships between data points, and adapts to new document formats without manual configuration.

The intelligence layer

What makes IDP "intelligent" is its combination of four core technologies:

• Optical Character Recognition (OCR) forms the foundation by converting printed and handwritten text from images into machine-readable data. But unlike standalone OCR, IDP's version is enhanced by AI to handle multiple languages and complex layouts.
• Natural Language Processing (NLP) identifies key information through named entity recognition, classifying elements like dates, amounts, and company names. It can identify that "due date" and "payment deadline" refer to the same concept, or that a string of numbers is an invoice ID rather than a phone number.
• Computer Vision analyzes document layouts and structures, particularly crucial for complex formats like invoices with tables. It recognizes tables, understands where key fields are typically located, and can even process documents it hasn't seen before by understanding visual patterns.
• Machine Learning ties everything together, continuously improving accuracy as it processes more documents. It learns from corrections, adapts to new formats, and becomes more efficient over time.

Beyond simple automation

IDP is a key component of broader digital transformation initiatives. While basic automation tools can handle structured data with predefined rules, IDP processes unstructured and semi-structured documents that make up 80-90% of business data.

This capability makes IDP essential for hyperautomation—where organizations aim to automate as many business processes as possible. By intelligently processing documents, IDP connects previously siloed processes, enables straight-through processing, and frees employees to focus on higher-value work.

Top 10 Intelligent Document Processing platforms in 2025

The IDP market has evolved significantly, with platforms now offering specialized capabilities for different business needs. Here's our analysis of the leading solutions, based on G2 ratings, market presence, and technological capabilities.

*Amazon Textract supports custom queries but not full user-trainable models.

Human-in-the-loop workflows in Textract are via integration, not native UI.

\ \ Nanonets \ \ Nanonets uses AI to extract data from documents automatically. It’s easy to integrate, customizable, and secure.](/content/buyers-guide/best-intelligent-document-processing-software#w-tabs-0-data-w-pane-0/index.html) \ \ UiPath Document Understanding \ \ UiPath Document Understanding adds AI-powered document extraction to UiPath’s RPA suite, ideal for structured and unstructured data in enterprise workflows.](/content/buyers-guide/best-intelligent-document-processing-software#w-tabs-0-data-w-pane-1/index.html) \ \ ABBYY FlexiCapture \ \ With 30+ years in OCR, ABBYY’s FlexiCapture blends legacy expertise with modern AI to deliver a robust, enterprise-grade IDP solution.](/content/buyers-guide/best-intelligent-document-processing-software#w-tabs-0-data-w-pane-2/index.html) \ \ Rossum \ \ Rossum uses cognitive AI to read documents like humans, outperforming template-based tools in handling diverse formats.](/content/buyers-guide/best-intelligent-document-processing-software#w-tabs-0-data-w-pane-3/index.html) \ \ Automation Anywhere \ \ Automation Anywhere’s IDP combines AI, NLP, and ML to extract data from any document type, seamlessly integrating with scalable business workflows.](/content/buyers-guide/best-intelligent-document-processing-software#w-tabs-0-data-w-pane-4/index.html) \ \ Appian Intelligent Document Processing \ \ Appian IDP integrates AI, workflow, and RPA into its low-code platform to streamline document handling within broader business processes.](/content/buyers-guide/best-intelligent-document-processing-software#w-tabs-0-data-w-pane-5/index.html) \ \ Hyperscience \ \ Hyperscience is an AI-powered IDP platform built for complex, high-volume enterprise workflows, with strong handwriting recognition and human-in-the-loop verification.](/content/buyers-guide/best-intelligent-document-processing-software#w-tabs-0-data-w-pane-6/index.html) \ \ Amazon Textract \ \ Amazon Textract is an AWS ML service that extracts text, tables, and key-value pairs from documents, ideal for cloud-first enterprises.](/content/buyers-guide/best-intelligent-document-processing-software#w-tabs-0-data-w-pane-7/index.html) \ \ Tungsten Automation (formerly Kofax) \ \ Tungsten Automation (formerly Kofax) offers enterprise-grade intelligent automation with strong document processing, workflow, and RPA capabilities.](/content/buyers-guide/best-intelligent-document-processing-software#w-tabs-0-data-w-pane-8/index.html) \ \ Microsoft AI Builder (with Power Automate) \ \ It lets you visually build, train, and drop AI models into your workflows, no code required, so you can extract insights, predict outcomes, and streamline processes.](/content/buyers-guide/best-intelligent-document-processing-software#w-tabs-0-data-w-pane-9/index.html)

Nanonets stands out as an AI-driven solution that automates document processing and data extraction workflows without requiring templates. Trusted by 34% of Fortune 500 companies, Nanonets specializes in handling unstructured document formats with high accuracy, making it ideal for organizations processing diverse document types.

Standout features

• Template-free document processing with advanced OCR and deep learning
• Intelligent workflow automation with real-time validation rules
• Pre-trained models for common document types (invoices, receipts, IDs)
• Human-in-the-loop verification for maintaining accuracy
• Support for 200+ languages
• Continuous learning from corrections

Ideal use case

Organizations processing high volumes of varying document formats, especially in finance, healthcare, and logistics.

Key integrations

• ERP systems: SAP, QuickBooks, Xero
• Cloud storage: Google Drive, Dropbox, OneDrive
• CRM: Salesforce
• Automation: Zapier for connecting to 3,000+ apps

Pricing

- Free plan available for basic needs and paid plans with custom pricing based on your needs

PROS

- Easy implementation with minimal technical expertise required

- Exceptional accuracy for unstructured documents

- Strong validation capabilities to ensure data quality

- Responsive customer support

- Handles complex, unstructured documents reliably

- Advanced table and line item recognition

CONS

- More expensive than basic OCR solutions (though ROI justifies cost)

- Some complex table structures may require initial training

UiPath Document Understanding extends the company's RPA platform with AI-powered document processing capabilities. It specializes in extracting data from both structured and unstructured documents, making it popular among enterprises already using UiPath's automation ecosystem.

Standout features

• AI and machine learning capabilities for handling unstructured documents
• Pre-trained extraction models for common document types
• Continuous improvement through machine learning model training
• Integration with UiPath's broader automation platform
• Human-in-the-loop validation for maintaining accuracy
• Strong OCR capabilities with support for multiple OCR engines

Ideal use case

Organizations processing diverse document types that want to integrate document extraction with end-to-end RPA workflows. Particularly effective for enterprises dealing with invoices, forms, and contracts that require data validation against backend systems.

Key integrations

• Native integration with UiPath RPA
• ERP systems (SAP, Oracle)
• Microsoft 365
• Various database systems

Pricing

- Part of UiPath's platform licensing

- Requires UiPath Automation Cloud or Enterprise licenses

- Custom pricing based on deployment scope

PROS

- Strong data extraction from unstructured documents

- AI and ML capabilities improve accuracy over time

- Seamless integration with UiPath automations

- Comprehensive workflow capabilities

CONS

- Initial setup and training can be complex for beginners

- Requires significant training data for optimal performance

- More expensive than standalone solutions

- Steep learning curve for non-technical users

With over 30 years in document recognition technology, ABBYY has evolved FlexiCapture into a comprehensive IDP solution that balances established OCR expertise with modern AI capabilities. The platform stands out for its enterprise focus and robust approach to document processing.

Standout features

• Document intake from virtually any source—scanners, email, mobile apps, or network folders
• Combination of traditional template-based extraction and AI-driven understanding
• Superior image processing that enhances poor-quality scans before extraction
• Thorough validation workflow with customizable business rules
• Browser-based verification stations for distributed teams
• Monitoring dashboards for tracking processing performance

Ideal use case

Organizations with established document workflows requiring high accuracy and compliance. Particularly strong in regulated industries like banking, insurance, and healthcare where audit trails and verification are critical.

Key integrations

• Connects with major RPA platforms
• Works with popular content management systems
• Functions with ERP and line-of-business applications
• Supports custom integrations through extensive APIs

Pricing

- Traditional enterprise licensing structure

- Options for perpetual licensing or subscription models

- Professional services typically required for implementation

PROS

- Exceptional accuracy across diverse document types

- Strong governance and compliance capabilities

- Scales effectively for high-volume processing

- Mature, battle-tested technology

CONS

- Implementation can be resource-intensive

- Premium pricing compared to newer cloud-based solutions

- Less nimble for rapidly evolving document requirements

- More IT-dependent than self-service platforms like Nanonets

Rossum takes a fundamentally different approach to document processing compared to template-based solutions. It uses cognitive AI to understand documents naturally—similar to how humans read them—making it particularly effective for organizations dealing with varying document formats.

Standout features

• Template-free cognitive data capture that eliminates manual template creation
• AI-powered validation with high accuracy out of the box
• Two-way communication system for efficiently handling exceptions
• Real-time collaboration tools for distributed teams
• Automated document routing and classification
• Developer-friendly API with extensive documentation
• Support for complex document types including invoices, POs, and receipts

Ideal use case

Organizations processing over 10,000 documents monthly who want to avoid the overhead of template management. Particularly suited for companies with diverse document formats from multiple vendors or those looking to modernize their document processing workflows.

Key integrations

• Native connections to major ERP and accounting systems
• Strong API capabilities for custom integrations
• Support for automated workflow systems

Pricing

- Custom enterprise pricing based on document volume and features

- Higher cost efficiency at larger document volumes

- Contact Rossum for detailed quotes

PROS

- Zero template maintenance required

- Quick implementation timeframe

- Excellent accuracy for standard documents

- Modern, intuitive interface

- Regular feature updates

- Responsive customer support

CONS

- Processing delays with large documents

- Higher costs for low volume processing

- Occasional system outages

- Limited Excel document support

- Complex tax handling for some regions

Automation Anywhere’s IDP solution is tightly integrated into its intelligent automation platform, bringing together advanced AI—including NLP, computer vision, and machine learning—to automate data extraction from virtually any document type. The system is designed for high scalability, secure processing, and seamless integration with end-to-end business workflows.

Standout features

• AI-powered extraction from structured, semi-structured, and unstructured documents (including images, tables, handwriting, barcodes, and audio/visual content)
• Plug-and-play tools with preset extraction packages for common scenarios
• Advanced OCR alongside NLP and machine learning
• Python scripting and custom logic to enhance extraction workflows
• Continuous learning from human feedback to improve accuracy over time
• Security and compliance guardrails, with audit-ready logs

Ideal use case

Organizations needing to automate high-volume, document-heavy processes end-to-end—such as accounts payable, claims management, or customer onboarding—especially where integration with RPA and business systems is key.

Key integrations

• Automation Anywhere’s RPA platform and Bot Store assets
• Wide range of ERP, CRM, and content management systems
• API and connector support for major enterprise platforms

Pricing

- Enterprise licensing and consumption-based models

- Custom pricing depending on document volume, features, and support

PROS

- Handles a huge variety of document types and formats

- Strong scalability for enterprise environments

- Continuous self-learning and improvement in extraction accuracy

- Flexible customization with scripting and AI workflow options

CONS

- Setup and optimization can require technical expertise

- Less focused on “out-of-the-box” IDP than some new entrants

- Some features may be overkill for simple automation needs

- Pricing can be complex depending on scale and requirements

Appian Intelligent Document Processing is an integrated part of Appian’s low-code automation platform, designed to streamline document handling as part of larger business processes. It combines AI, workflow, and RPA capabilities, emphasizing collaboration and process flexibility.

Standout features

• Built-in AI for document classification and data extraction, including OCR and support for dates, names, addresses, and more
• Highly collaborative interface for teams working on the same resources
• Low-code design for intuitive workflow automation and rapid solution development
• Strong API integration for connecting to external and legacy systems
• Document creation, classification, and formatting tools
• Document retention, archiving, and security management

Ideal use case

Organizations seeking to automate and orchestrate document-driven workflows across teams, requiring both IDP and workflow/case management. Particularly suited when collaboration and flexibility are important, and when integration with legacy systems is required.

Key integrations

• Appian RPA and workflow modules
• API and integration objects for ERP/CRM/content management
• Supports integration with other OCR engines and legacy platforms

Pricing

- Subscription pricing based on users and applications

- Custom quotes depending on deployment size and required modules

PROS

- User-friendly interface, especially for low-code automation

- Facilitates easy collaboration on shared resources

- Flexible and customizable for various business needs

- Good for rapidly developing and deploying new document workflows

- Reliable for structured document classification and extraction

- Can reduce development effort for common process automations

CONS

- Struggles with complex document layouts or highly unstructured data

- Average performance on handwritten recognition compared to specialized tools

- Limited support for rich media (charts, videos, images)

- Onboarding and learning curve can be steep for first-time users and developers

- Some configuration and client requirements are difficult to fulfill for advanced use cases

- Language support has limitations versus some competitors

Hyperscience is recognized for its AI-driven back-office automation and intelligent document processing, targeting high-volume, complex workflows in enterprise environments. The platform is praised for its handwriting recognition, structured and unstructured data extraction, and human-in-the-loop design for data verification.

Standout features

• Machine learning models that improve with user feedback and exception handling
• Easy onboarding for structured documents; intuitive UI for layout setup and supervision
• Human-in-the-loop review and feedback for ongoing accuracy improvements
• Parallel document processing for large batches; scalable and flexible deployment
• Confidence scoring, transparency, and detailed reporting on automation vs. manual rates
• Support for multiple languages including Arabic, German, English, French, Italian, Korean, Dutch, Portuguese, and Spanish

Ideal use case

Large enterprises in banking, insurance, healthcare, and government that need to automate repetitive document processing, especially where form types are stable and high accuracy is required.

Key integrations

• APIs for seamless integration with core systems
• Native support for RPA and downstream process automation
• Data export for analytics platforms

Pricing

- Custom enterprise pricing

- Based on volume and deployment scope

PROS

- Highly accurate on both structured and semi-structured content

- Excellent at handwriting and low-quality scan extraction

- Intuitive, user-friendly interface

- Strong customer support and onboarding experience

- Document-level transparency and manual validation tools

CONS

- Requires significant training data for semi-structured document models

- Pre- and post-processing can be time consuming for edge cases

- Maximum file/page limits may be restrictive for some use cases

- Advanced customization sometimes needed for unique workflows

- Documentation and self-serve resources could be improved

Amazon Textract is a fully managed machine learning service from AWS that extracts text, tables, and key-value pairs from scanned documents, forms, and a variety of business records. Its native integration with AWS services and scalable infrastructure make it a popular choice for enterprises operating in the cloud.

Standout features

• Accurate extraction of printed text, forms, tables, and key fields from diverse formats (IDs, diplomas, insurance forms)
• Confidence scores included with extracted data for automation and review
• End-to-end encryption, with compliance for industry standards
• Scalable serverless processing—handles large or unpredictable document volumes
• Integration with AWS services (Lambda, S3, Step Functions, Comprehend)
• No need for custom OCR tool development or template libraries

Ideal use case

Organizations operating on AWS with high-volume, cloud-native document processing needs, especially where integration with other AWS services is critical. Well-suited for automating claims processing, loan applications, onboarding, and extracting structured data from forms at scale.

Key integrations

• Native with AWS ecosystem (S3, Lambda, Redshift, Comprehend, SageMaker)
• API for custom application integration
• Can be linked to downstream analytics and workflows using AWS tools

Pricing

- Pay-as-you-go based on document volume and features used

- Can become expensive for very high volumes or complex extraction needs

- No free trial, but low barrier for initial testing

PROS

- Accurate at extracting data from structured, printed documents

- Secure by default, with encryption at rest and in transit

- Strong documentation and API support

- No manual template setup required

- Deep integration with AWS ecosystem for workflow automation

- Supports multiple languages and can process both typed and handwritten inputs

- Scalable for processing large document volumes

CONS

- Struggles with unusual layouts, complex or rotated documents, and messy handwriting

- Language support primarily optimized for English; limited non-English capabilities

- Expensive for large volumes or real-time, high-frequency workloads

- Requires external post-processing for some advanced needs

- Lacks some advanced workflow automation and user-friendly configuration features found in specialized IDP platforms

Standout features

• Direct editing and annotation of PDFs
• Electronic signatures and workflow support
• OCR and zone-based data extraction (including handwriting recognition)
• Scripting and out-of-the-box utilities for advanced extraction
• Microsoft Office integration right from the toolbar
• Centralized configuration and scalable deployment options

Ideal use case

Best suited for organizations with high-volume document capture needs or those requiring scriptable, template-based extraction. Well-suited for financial services, government, legal, and back-office operations processing structured forms, invoices, or regulated paperwork.

Key integrations

• Microsoft Office
• SAP
• SharePoint
• Supports scripting automation with .NET and VB

Pricing

- Pricing is customized based on deployment, user count, and solution mix. Per-seat pricing is known to be competitive, especially at volume. Contact sales for a custom quote.

PROS

- PDF and image editing in one suite

- Easy to install, rarely needs support

- Accurate OCR and handwriting tools

- User-friendly for basic tasks

- Centralized, scalable management

- Customizable extraction and scripting

- Fast, efficient for high volumes

CONS

- Documentation and community are lacking

- Relies on resellers for support

- Formatting can break after conversion

- Needs expertise for setup and advanced use

- Interface can feel dated and navigation clunky

- Custom integrations and scripting can be complex

- Not web-based (fat client), limited cloud-native options

10. Microsoft AI Builder (with Power Automate)

Microsoft AI Builder is a low-code AI solution within the Power Platform that enables business users to build, train, and deploy document processing models—directly integrated with Power Automate. This combination brings AI-powered document understanding to everyday workflows in Microsoft 365, Dynamics, and beyond, making automation accessible for both IT and non-technical users.

Standout features

• Prebuilt models for form, invoice, and receipt extraction; ID and business card reading
• Trainable custom models: upload your own samples and teach the AI to extract specific data
• Drag-and-drop automation with Power Automate flows for approvals, notifications, and integrations
• Native integration with Microsoft Teams, SharePoint, Outlook, OneDrive, and Excel
• Cloud-based, with secure data handling and compliance support
• Audit trails, error handling, and customizable business rules as part of workflow design

Ideal use case

Organizations already invested in Microsoft 365 or Dynamics, especially those wanting to automate document capture, approval, and data entry without writing code. Frequently used in finance, HR, sales, and operations to process invoices, contracts, forms, and onboarding documentation.

Key integrations

• Power Automate for no-code/low-code business process workflows
• Power Apps for custom business app development
• Direct connectors for hundreds of business and productivity tools, including ERPs and CRMs

Pricing

- Consumption-based licensing, or as capacity add-ons to Microsoft 365/Dynamics subscriptions

- Included with select enterprise Microsoft plans

PROS

- Very smooth integration with Microsoft ecosystem and connectors

- No-code setup empowers business users and “citizen developers”

- Prebuilt use cases mean fast time-to-value for standard document types

- Scalable cloud solution, with ongoing updates and security from Microsoft

CONS

- Less flexibility and customizability than IDP specialist platforms

- Performance and accuracy are best on structured forms and standard documents

- Advanced or highly complex document extraction may require more specialized tools

- Some workflows may still need manual review for edge cases or non-structured data

How industries are using Intelligent Document Processing

"RoI is too high to even quantify. We get 400 invoices a day and it takes our team 10 minutes to process them. It's hard to even look back at our previous manual process. You're talking hundreds if not thousands of hours a year that is being saved by this process." explains the Head of IT of a mid-sized Property Management firm who reduced invoice processing costs by 80% through Nanonets IDP implementation.

This experience mirrors thousands of organizations across industries that have discovered IDP isn't just about cost-cutting—it's about transforming document-intensive processes into strategic advantages.

Here's how different sectors are putting this technology to work:

Finance and accounting

Finance teams process thousands of documents daily - from invoices and receipts to purchase orders and bank statements. Modern IDP platforms like Nanonets can process complex financial documents with 95%+ accuracy in document structure recognition.

Key applications:

• Automated invoice data capture and entry
• Processing and validation of purchase orders
• Accounts payable and receivable automation
• Extraction of line items from receipts and expense reports
• Processing tax forms and supporting compliance documentation
• Financial statement digitization and audit support

Healthcare

Healthcare organizations deal with massive volumes of patient records, insurance claims, and clinical documentation. IDP helps maintain HIPAA compliance while streamlining document processing.

Key applications:

• Insurance claims processing
• Electronic Health Record (EHR) data extraction
• Patient documentation management
• Medical billing automation

Legal services

Law firms and legal departments use IDP to process contracts, court documents, and compliance records. The technology is particularly valuable for contract analysis, where it can extract key clauses, dates, and obligations automatically.

Key applications:

• Contract analysis and management
• Legal document review
• Compliance monitoring
• Case document processing

Logistics and supply chain

With global supply chains generating countless shipping documents, IDP helps logistics companies process documentation efficiently while reducing errors.

Key applications:

• Bills of lading processing
• Customs documentation
• Shipping manifests
• Delivery confirmations

In each industry, IDP delivers tangible improvements in accuracy, efficiency, and compliance. This allows organizations to scale their document processing capabilities while freeing staff to focus on strategic work.

Essential features to look for in an IDP platform

As IDP technology rapidly evolves, the gap between leading platforms and legacy solutions continues to widen. The features that defined cutting-edge systems just three years ago are now standard offerings, while new capabilities like zero-shot learning, multi-modal understanding, and autonomous document classification have emerged as key differentiators.

Understanding these technological advances is crucial for making forward-looking implementation decisions:

OCR accuracy and language support

• Base OCR accuracy should exceed 95% for standard documents
• Support for multiple OCR engines (like PaddleOCR and DocTR) for optimizing different document types
• Ability to handle multiple languages, including non-Latin scripts
• Performance with low-quality scans and handwritten text

AI model flexibility

• Pre-trained models for common document types (invoices, receipts, IDs)
• Custom model training capabilities for unique document formats
• Continuous learning from corrections and feedback
• No template requirements for new document formats

Intelligent data capture

• Automatic document classification and routing
• Named entity recognition for key information extraction
• Table structure recognition and complex layout handling
• Validation rules and data accuracy checks

Human-in-the-loop capabilities

• User-friendly interface for reviewing extracted data
• Confidence scores for extracted fields
• Quick correction tools for misidentified information
• Learning from human corrections to improve accuracy

Integration capabilities

• API access for custom integrations
• Native connectors to major ERP systems
• Integration with RPA platforms
• Support for popular accounting software

Security and compliance

• Role-based access control
• Audit trails for all document processing
• Data encryption at rest and in transit
• Compliance with industry standards (HIPAA, GDPR)

Processing speed and scalability

• Real-time processing capabilities
• Batch processing for high volumes
• Performance monitoring tools
• Scalable infrastructure for growing needs

When comparing platforms, prioritize features that align with your specific document processing requirements and volume. The best solution will balance advanced automation capabilities with practical usability for your team.

How to choose the right IDP solution for your business

Building on the features we've explored, the next challenge becomes matching these capabilities to your specific business requirements. We’ve created a decision matrix that maps critical organizational needs to corresponding IDP functionality, helping you navigate the complex landscape of available solutions.

By systematically evaluating platforms against your document processing requirements, you can avoid the costly mismatch that derails many implementation projects.

Monthly volume

• Basic: Less than 1,000 documents per month
• Advanced: More than 10,000 documents per month

Document variety

• Basic: Single language, 2–3 document types
• Advanced: Multiple languages, 10+ document types

Format types

• Basic: Primarily digital PDFs or images
• Advanced: Mix of scanned, digital, and handwritten documents

OCR accuracy

• Basic: 90–95% accuracy for standard formats
• Advanced: 95%+ accuracy even for complex documents

Processing speed

• Basic: Within 24 hours
• Advanced: Real-time or near real-time turnaround

Security needs

• Basic: Basic encryption and role-based access control
• Advanced: SOC 2, HIPAA, GDPR compliance, plus on-premises deployment options

Implementation approach

• Basic: Vendor-managed, cloud-based solutions
• Advanced: Self-hosted or custom enterprise deployments

Technical resources

• Basic: Minimal IT support required
• Advanced: Needs a dedicated technical team

Integration needs

• Basic: Standard connectors and REST API access
• Advanced: Custom connectors, API development, support for Zapier/Webhooks

AI capabilities

• Basic: Pre-trained models for common document types
• Advanced: Zero shot models and ability to train custom models for specialized documents

Budget range

• Basic: Pay-per-use or entry-level plans
• Advanced: Enterprise licensing, custom contracts

Export formats

• Basic: CSV, JSON
• Advanced: Custom formats with real-time sync to business systems

Implementation approach

Consider starting with a proof of concept on a specific document type or department before rolling out across your organization. This approach helps validate the solution's effectiveness for your specific use case while minimizing risk.

Key factors to evaluate during your proof of concept:

• Accuracy rates for your specific documents
• Processing speed at your required volume
• Ease of integration with existing systems
• User experience for your team
• Quality of vendor support

IDP implementation checklist

One recent study indicated that organizations implementing IDP reported average payback periods of 8.5 months with annual ROI of 245% post-initial payback. However, these results varied widely based on implementation methodology. Implementation approach significantly impacts IDP success.

This evidence underscores why methodology matters as much as technology in IDP success:

• What specific document challenge are you trying to solve?

Document the exact pain points – processing delays, error rates, compliance issues, or manual effort. When your document challenges are clearly defined, it often leads to better implementation and higher satisfaction.

• Have you gathered baseline metrics for current processing?

Measure current processing times (e.g., 15 minutes per invoice), costs per document, error rates, and compliance issues. These metrics will be essential for calculating ROI and proving success.

• Have you identified the right stakeholders for your IDP project?

Include an executive sponsor, process owners, IT representatives, end users, and compliance officers. According to implementation studies, cross-functional teams achieve implementation success rates 2.5x higher than IT-only projects.

• Has the vendor offered a formal POC contract?

Reputable IDP vendors offer structured POC agreements with clear terms, success metrics, and evaluation criteria. Look for vendors providing 2-4 week POC periods, technical support during testing, and a defined path to production if successful. Test with 100-200 real-world documents to accurately assess performance.

• Have you collected sufficient training documents? Gather 50-100 examples of each document type, representing the full range of formats, qualities, and variations you typically receive. For custom documents, more samples may be needed (research suggests 500+ for highly variable formats). Some vendors may offer zero-shot models, which may not need many training documents.

• Have you evaluated vendor onboarding and training support?

Compare vendor onboarding packages, including implementation assistance, training sessions, documentation, and ongoing support options. Ask about dedicated implementation managers, knowledge transfer practices, and training for both administrators and end users.

• Have you mapped your integration requirements?

Document exactly how documents enter your system (email, scans, uploads) and where extracted data needs to go (ERP, accounting system, databases). Identify APIs, connectors, and security requirements for each integration point.

• Have you designed your exception handling workflow?

Create specific processes for documents the system can't automatically process. Define confidence thresholds, human review protocols, and feedback loops to improve system accuracy over time.

• Have you created a phased rollout schedule?

Plan a gradual implementation starting with one document type. Organizations that implement in phases to achieve positive ROI faster than those attempting full-scale deployment immediately.

• ‍ Have you developed user adoption strategies? Plan how you'll transition users to the new system, including training sessions, documentation, helpdesk support, and feedback mechanisms. Consider "champions" within departments who can support colleagues during the transition.

• Have you established an ROI measurement framework?

Define exactly how you'll calculate return on investment, including direct savings (labor costs, error reduction), indirect benefits (faster processing, improved compliance), and target metrics for each phase. Research shows the average payback period is 8.5 months.

• Have you scheduled regular performance reviews?

Plan 30, 60, and 90-day review sessions to evaluate system performance, user adoption, and business impact. Use these sessions to identify optimization opportunities and plan expansion to additional document types.

Common pitfalls in IDP implementations

While implementation studies show a 93.78% reduction in processing time is possible with IDP, real-world deployments often fall short of vendor promises. Organizations often encounter significant challenges that delay their expected ROI timeline.

This gap between expectations and reality highlights why understanding common pitfalls is crucial before starting your IDP journey.

• Template dependency Many organizations initially select template-based approaches because they seem simpler. However, these systems break down when confronted with new document formats, requiring constant maintenance and reconfiguration. Template-free, AI-driven approaches provide more flexibility but require more advanced implementation planning.

• Inefficient exception handling It's relatively easy to get 80% of your documents processed automatically. It's that last 20% that gets expensive. Companies often end up with a hybrid process where the straightforward documents flow through smoothly while the exceptions require even more manual effort than before. Ensure the vendor you choose has a solid exception handling workflow and you plan exceptions from day one.

• Integration headaches Your IDP solution needs to share and receive data from your other systems, and this is where things often break down. You don’t want to spend weeks implementing an IDP solution only to discover your ERP couldn't accept the processed data in a usable format. Make sure you test the entire data flow, not just the document processing piece

• Poor visibility Once documents enter an automated system, users often lose visibility into where things stand. This leads to duplicate submissions and workarounds that actually create more work. Good implementations include status tracking and visibility tools for both processors and requestors

• Siloed implementations When IDP projects are treated as isolated technology initiatives rather than business process transformations, they often fail to deliver expected value. Cross-functional involvement is essential for addressing process changes, user impacts, and workflow adjustments.

• Feedback loop failures IDP systems improve through human feedback. Without structured processes for users to correct extraction errors and feed this information back to the system, accuracy plateaus instead of improving over time.

• Regulatory blindspots

Organizations sometimes implement IDP without fully considering regulatory requirements around data privacy, retention, and security. This oversight can create compliance risks, especially in regulated industries like healthcare and financial services.

• Vendor dependency Companies often rely on consultants or vendors for implementation without developing internal expertise. When those experts leave, the system becomes a mysterious black box that no one can maintain or troubleshoot. Build internal knowledge transfer into your implementation plan.

How to calculate ROI for intelligent document processing

AI-driven IDP solutions have demonstrated significant financial impact in real-world deployments. For example, Anchoori et al. (2024) document a 95% reduction in processing time, a 62% drop in operational costs, and payback periods averaging 8.5 months in enterprise-scale projects. But to build a business case for your own organization, you'll need to quantify these results using your actual data.

1. Establish your current baseline

• Measure average cost per document: Include labor time, exception handling, error correction, and compliance management.

• Track processing times: How long from document receipt to usable data?

• Identify error and exception rates: What proportion of documents require manual review or rework?

Example: A finance team finds that invoice processing costs $7.25 per document, with 12 minutes of staff time spent on each invoice and a 3% error rate resulting in extra rework.

2. Quantify the impact of automation

• Estimate labor savings based on time reduction benchmarks (research shows 95–98% faster processing).

• Calculate projected error rate reduction (manual: 2–4% error; IDP: documented exceptions below 1% for structured docs).

• Factor in reduced costs for compliance breaches, late processing, and penalties, if applicable.

Example: After deploying IDP, that same finance team reduces average invoice processing time to under 1 minute, cuts the error rate to 0.5%, and lowers cost to $1.10 per document.

3. Calculate investment and recurring costs

• Include software licensing or subscription, setup and integration, user training, and any infrastructure upgrades.
• Account for ongoing maintenance, expansion, and periodic pricing updates.

Example: A mid-sized company invests $20,000 for the first year (implementation, integration, and 12-month license or subscription), and expects $35,000 in annual recurring costs, which covers license renewal, support for new documents, and model tuning.

4. Project the payback period and ROI

• Average payback period reported: 8.5 months. Calculate your own by dividing total project cost by annual savings.
• Formula for ROI:

ROI = (Annual Savings − Annual Costs) ÷ Annual Costs × 100

Example: If IDP saves $160,000 in labor and error correction each year:

• Payback period = $60,000 ÷ $160,000 = 0.375 years (~4.5 months)
• Annual ROI = ($160,000 − $15,000) ÷ $15,000 × 100 ≈ 967%

5. Build your business case

• Present before-and-after scenario metrics, using conservative estimates based on benchmarks in published research.

• Highlight direct savings (labor, error correction, compliance) and indirect value (staff redeployment, faster customer/vendor response).

• Tie your projections to established benchmarks where possible:

• Processing time reduction: 95%+

• Cost reduction: 62% (Anchoori et al., 2024)

• Error rate improvement: up to 85% reduction in exceptions

6. Monitor and optimize post-implementation

• Track actual post-IDP metrics against your projections.

• Refine business case and projections for future phases based on real observed improvements.

• By grounding projections in actual operational data and industry benchmarks, you can make a credible case for the value of IDP—one that stands up to both financial and operational scrutiny.

Security and compliance essentials for IDP implementations

When the healthcare provider, Defined Physical Therapy, implemented Nanonets IDP, they reduced turnaround time for the insurance claim process from 11 minutes to 1.25 minutes. But their primary concern wasn't just efficiency—it was maintaining HIPAA compliance while handling this sensitive data at scale. Their experience highlights why security and compliance considerations must be central to any IDP project from day one.

Regulatory compliance requirements

Different industries face unique compliance challenges when implementing IDP:

Healthcare (HIPAA):

• Patient data must be encrypted both in transit and at rest
• Audit trails must track all document access and modifications
• Business Associate Agreements (BAAs) required with IDP vendors
• Data retention policies must align with regulatory requirements

Financial services (SOX, PCI-DSS):

• Strict access controls for financial documents
• Comprehensive audit trails for all document processing
• Secure handling of payment information and account data
• Data sovereignty requirements for cross-border processing

Global operations (GDPR, CCPA):

• Right to be forgotten capabilities
• Data minimization principles in document storage
• Consent management for personal data processing
• Cross-border data transfer restrictions

Deployment models and security implications

Cloud deployments:

• Faster implementation and lower initial costs
• Vendor responsibility for infrastructure security
• Regular security updates without IT intervention
• Potential data residency challenges in multi-national operations

On-premises deployments:

• Complete control over infrastructure and security measures
• Data never leaves your environment
• Compliance with strict data sovereignty requirements
• Higher IT maintenance burden and infrastructure costs

Hybrid models:

• Processing occurs on-premises while leveraging cloud capabilities
• Sensitive data remains within your security perimeter
• Flexibility to adapt to varying compliance requirements
• More complex implementation and maintenance

Essential security features for IDP platforms

Access control:

• Role-based access control (RBAC) with principle of least privilege
• Single sign-on (SSO) integration with existing identity providers
• IP-based access restrictions for sensitive documents
• Session timeout and automatic lockout features

Data protection:

• End-to-end encryption for documents in transit
• At-rest encryption with customer-managed keys
• Secure document disposal after processing when required
• PII/PHI detection and automatic redaction capabilities

Audit and compliance:

• Comprehensive audit logs of all document access and actions
• Tamper-proof record keeping for compliance verification
• Regular penetration testing and security assessments
• Compliance certifications relevant to your industry (SOC 2, ISO 27001)

Vendor security assessment checklist

When evaluating IDP vendors, verify these security credentials:

• SOC 2 Type II certification
• HIPAA compliance (with signed BAA if applicable)
• GDPR compliance documentation
• Regular independent security audits
• Data breach notification procedures
• Recovery point objective (RPO) and recovery time objective (RTO)
• Data retention and destruction policies

Organizations implementing IDP should work closely with their security, legal, and compliance teams throughout the selection and implementation process. The most successful deployments incorporate security requirements into the initial planning rather than addressing them as an afterthought.

Integrations: Where IDP fits in your tech stack

When SaltPay needed to process thousands of vendor invoices through their new SAP system, manual data entry wasn't an option. By implementing Nanonets' IDP solution with direct SAP integration, they achieved "100% automation" of their accounts payable process.

"Nanonets is like magic. I can't imagine how I would do invoice extraction without it," explains SaltPay's Accounts Payable Financial Analyst .

This implementation story demonstrates how IDP becomes exponentially more valuable when integrated with existing business systems.

a. RPA platforms

IDP and Robotic Process Automation (RPA) create a powerful combination. IDP handles unstructured document data while RPA automates the structured workflows that use this data.

UiPath integration:

• UiPath's Document Understanding technology leverages AI to process and categorize documents automatically. Its drag-and-drop interfaces (Studio and StudioX) enable creation of end-to-end document workflows without coding expertise.
• For example, an invoice processing solution might use Document Understanding to extract invoice data, then UiPath's Process Mining to optimize the validation against purchase orders and payment initiation.

Automation Anywhere:

• Automation Anywhere combines RPA with AI-driven analytics and cognitive bots to orchestrate document-centric processes. As documented in the ERPA research, integrating IDP with Automation Anywhere can reduce processing time compared to standalone document processing, particularly for complex workflows involving multiple systems.

Integration considerations:

• API compatibility between your IDP and RPA platforms
• Document queue management between systems
• Exception handling workflows
• Authentication and security alignment

b. ERP systems

Most document data ultimately needs to reach your ERP system, making this integration critical for operational efficiency.

SAP integration:

• SaltPay's experience demonstrates the power of SAP integration with IDP. Their implementation captures invoice data and transfers it directly into SAP, with built-in validation based on business rules. Leading IDP platforms like Nanonets offer pre-built connectors for SAP S/4HANA and earlier versions, enabling direct data transfer from documents to SAP transactions.

NetSuite and other cloud ERPs:

• Cloud-based ERPs like NetSuite provide REST APIs and integration frameworks that IDP solutions can use for real-time data transfer. These integrations typically rely on third-party connectors or custom development to enable both batch processing and real-time document workflows, especially for specialized document types.

Integration considerations:

• Field mapping between document data and ERP structures
• Master data alignment (vendor records, GL codes)
• Transaction validation requirements
• Approval workflow integration

c. CRM platforms

Document data can enrich customer records and streamline customer-facing processes when integrated with your CRM.

Salesforce integration:

• IDP solutions can extract data from customer documents and update Salesforce records through the Salesforce API. This streamlines processes like contract management, customer onboarding, and support documentation. Salesforce Flow can then automate subsequent actions based on the extracted document data.

HubSpot and marketing automation:

• Document data can trigger marketing workflows, update contact records, and enhance customer segmentation when integrated with marketing automation platforms.

Integration considerations:

• Customer record matching logic
• Document classification for appropriate routing
• Security and compliance for customer data
• Automation trigger configuration

d. Low-code platforms

Low-code platforms enable custom integration pathways between IDP and other systems without extensive development resources.

Microsoft Power Automate:

• Microsoft Power Automate offers AI Builder for form processing and connects document processing to hundreds of business applications through pre-built connectors. This enables organizations to create document workflows that integrate with Microsoft 365 apps and third-party systems without deep technical expertise.

Appian:

• Appian's low-code automation platform combines process management capabilities with the ability to incorporate IDP through integration with AI services or third-party providers. This allows organizations to build document-centric workflow applications that connect to enterprise systems with minimal coding.

Integration considerations:

• Connector availability for your specific systems
• Transaction volume limitations
• Error handling capabilities
• Governance and security controls

Integration best practices

Regardless of which systems you're connecting, follow these integration principles:

• Start with the end-to-end process in mind: Map the complete document journey before configuring individual integrations.

• Implement proper error handling: Create clear procedures for integration failures and data mapping issues.

• Consider volume and latency requirements: Ensure your integration approach can handle peak document volumes with acceptable processing times.

• Plan for testing across systems: Develop test cases that verify the entire process flow, not just individual connections.

• Document integration architecture: Maintain clear documentation of integration points, data mappings, and dependencies for future maintenance.

By thoughtfully integrating IDP with your existing technology stack, you transform it from a standalone solution to a critical component of your business process architecture.

Frequently Asked Questions (FAQs)

This is some text inside of a div block.

Thank you! Your submission has been received!

Oops! Something went wrong while submitting the form.

What is intelligent document processing software?

Intelligent Document Processing (IDP) software is an advanced AI solution automating the extraction, processing, and understanding of data from diverse document formats. It goes beyond simple text recognition. Unlike traditional OCR, IDP combines Machine Learning (ML), Computer Vision (CV), and Natural Language Processing (NLP) to:

• Comprehend Document Context: Understand the meaning and purpose of a document (e.g., an invoice versus a contract).
• Identify Relevant Information: Precisely locate and pull out specific data fields (like an invoice total or a contract clause) based on their meaning, not just fixed positions.
• Transform Unstructured Data: Convert data from varied layouts, handwritten notes, or scanned documents into clean, structured, and usable formats.

IDP is essentially giving computers the ability to "read" and "understand" documents, turning static information into actionable insights for automated workflows.

How does IDP differ from OCR?

OCR (Optical Character Recognition) is foundational, like a digital eye. It simply converts printed or handwritten text images into machine-readable digital text. It sees characters.

IDP (Intelligent Document Processing) builds upon OCR, acting like a digital brain. It takes OCR's output and then uses AI (ML, NLP, CV) to go several crucial steps further:

• Contextual Understanding: IDP understands the document's content and overall structure (e.g., this isn't just text; it's an invoice, and this specific number is the invoice total).
• Semantic Extraction: It extracts specific data points based on their meaning and relationships (e.g., pulling "invoice number" and linking it to the correct "vendor"), not just where text is located.
• Automated Classification: IDP automatically identifies the document type (e.g., "invoice," "receipt," "contract") and can even sub-classify (e.g., "PO-backed invoice").
• Adaptive Learning: It continuously improves its accuracy by learning from human corrections and new document variations, adapting over time.
• Complex Layout Handling: IDP processes intricate layouts like tables, forms, and free-form text, maintaining structural integrity (e.g., correctly extracting all line items from a complex invoice table).
• Template-Free: Unlike traditional OCR, IDP doesn't require rigid templates for each document layout. It adapts to variations automatically.

Think of it this way: OCR "reads" the words. IDP "understands" the document.

Is IDP suitable for small businesses?

Yes, IDP is increasingly suitable for small businesses, but with considerations. While traditionally viewed as an enterprise solution, cloud-based IDP platforms and flexible pricing models have made it more accessible.

Benefits for Small Businesses:

• Efficiency Gains: Small businesses often suffer disproportionately from manual, document-heavy tasks (e.g., processing vendor invoices, managing customer onboarding paperwork) because they lack large administrative teams. IDP dramatically reduces this burden.
• Cost Savings: By automating data entry, IDP can reduce processing costs per document, freeing up valuable staff time or preventing the need for new hires.
• Accuracy: Reduces costly manual errors in financial records or customer data.
• Accessibility: Cloud-based IDP solutions with pay-per-use or tiered pricing (like Nanonets) make the technology financially accessible. You pay only for what you process.
• Improved Compliance: Helps smaller businesses maintain organized records for audits.

Considerations for Small Businesses:

• Focused Use Case: Start with a clear, high-volume use case that delivers immediate ROI, such as accounts payable automation (e.g., processing all vendor invoices) or automating customer onboarding.
• Complexity: If your documents are highly unique or extremely unstructured, initial setup might require more effort or customization.
• Integration: Ensure the IDP solution integrates easily with your existing small business accounting software (e.g., QuickBooks, Xero) or CRM.

For small businesses dealing with repetitive document processing, a focused IDP implementation, particularly with user-friendly, cloud-based platforms, can deliver significant value and clear ROI.

How secure is intelligent document processing?

Modern Intelligent Document Processing (IDP) platforms incorporate multiple, robust security layers to protect sensitive document data throughout its lifecycle. Enterprise-grade solutions prioritize security as paramount, especially when handling financial, personal, or proprietary information.

Key security elements these platforms typically offer:

• End-to-End Encryption:

• Data in Transit: Documents and extracted data are encrypted (e.g., using TLS 1.2+ / HTTPS) as they travel between your systems and the IDP platform.

  • Data at Rest: Data temporarily or persistently stored on the IDP provider's servers is encrypted (e.g., using AES-256 encryption).

• Compliance with Regulations & Certifications:

• Reputable vendors adhere to major data privacy regulations like GDPR (General Data Protection Regulation) and CCPA (California Consumer Privacy Act).

  • They often hold industry-specific security certifications like SOC 2 Type II (evaluates data handling across security, availability, processing integrity, confidentiality, and privacy), ISO 27001 (information security management), and, for healthcare data, HIPAA compliance.
  • Nanonets explicitly prioritizes and achieves these stringent compliance standards.

• Access Controls:

• Role-Based Access Control (RBAC): Limits who can view, access, and process documents or extracted data within the platform based on their defined roles and permissions.

  • Strict Internal Controls: The provider implements rigorous internal controls and audits over their own staff's access to customer data.

• Secure Infrastructure:

• Cloud-based IDP solutions are typically hosted on major cloud providers (e.g., AWS, Google Cloud, Azure) known for their state-of-the-art physical and network security infrastructure.

• Data Minimization and Retention Policies: Providers have clear, transparent, and often configurable policies on how long data (especially original document images) is stored. For many setups, documents are processed and then deleted immediately or after a short, defined validation period. This minimizes long-term risk.

• Audit Logs:

• Comprehensive, immutable audit logs track every document access, data extraction, modification (including human corrections), and processing activity. This provides transparency and accountability, crucial for internal security monitoring and external audits.

• On-premises Deployment Options:

• For organizations with extremely sensitive data or strict data residency requirements, some IDP providers (like Nanonets) offer private cloud or on-premises deployment options, allowing maximum control over data.

By implementing these comprehensive security measures and adhering to robust compliance frameworks, modern IDP platforms ensure sensitive document data is protected throughout the automation lifecycle.

Can IDP work with handwritten documents?

Yes, Intelligent Document Processing (IDP) can work with handwritten documents, but with lower accuracy compared to clear, machine-printed text. Modern IDP systems use advanced Artificial Intelligence (AI) techniques to recognize handwriting, pushing the boundaries of what's possible.

Here's how IDP handles handwriting:

• Handwritten Text Recognition (HTR): IDP platforms integrate specialized HTR engines, which are a form of advanced AI. Unlike basic OCR that primarily focuses on printed fonts, HTR models are trained on vast datasets of diverse handwriting styles, allowing them to interpret a wide range of penmanship.
• Contextual Understanding: Even if individual handwritten characters are ambiguous, the IDP's Machine Learning (ML) and Natural Language Processing (NLP) components use context to infer meaning. For example, if a word looks like "date" and is next to numbers, the AI is more likely to correctly interpret it as a date.
• Image Pre-processing: Advanced IDP solutions apply robust image enhancement techniques to scanned or photographed handwritten documents. This includes de-skewing, de-noising, and improving contrast to make the handwriting clearer for the HTR engine.
• Accuracy for Handwriting: Accuracy varies significantly based on handwriting clarity, neatness, and the system's training. For clear, print-style handwriting, HTR can achieve accuracies typically ranging from 80-90%. For messy or cursive handwriting, accuracy may drop to 50-70% or lower.
• Role of Human-in-the-Loop (HITL): For critical data extracted from handwritten documents, human review is essential to ensure accuracy. Most organizations implement a Human-in-the-Loop (HITL) process. If the AI has low confidence in a handwritten extraction, or if it fails a validation rule, it's flagged for a human to quickly review and correct. This ensures 100% data accuracy for sensitive information. Crucially, these human corrections feed back into the AI model (as supported by Nanonets), continuously improving its HTR capabilities and overall accuracy for your specific handwriting styles over time.

While processing handwriting remains more challenging than printed text, IDP significantly automates the task, reducing manual effort and making previously inaccessible handwritten data usable.

How long does it take to implement an IDP system?

The implementation timeline for an IDP (Intelligent Document Processing) system varies based on complexity, ranging from a few weeks for basic use cases to several months for complex, enterprise-wide deployments.

General timelines:

• Basic Implementation (2-4 weeks):

• Scope: Automating a single, standard document type (e.g., common vendor invoices, simple forms) with pre-built models.

  • Steps: Includes initial platform setup, basic integration with a single system, minimal AI configuration using existing models (e.g., using Nanonets' pre-trained invoice model), and quick testing.
  • Benefit: Rapid time-to-value, quick ROI demonstration.

• Moderate Implementation (1-3 months):

• Scope: Automating multiple document types, or custom document types requiring dedicated AI model training. Involves a few key integrations.

  • Steps: Includes detailed process assessment, custom AI model training (e.g., training Nanonets' AI on unique internal forms or less common supplier invoices), configuring more complex validation rules, setting up multiple integration points, and user training.
  • Benefit: Addresses more significant bottlenecks with customized accuracy.

• Complex Implementation (3-6 months+):

• Scope: Enterprise-wide deployment involving high volumes of diverse, unstructured documents (e.g., contracts, claims, scientific reports), multiple complex integrations (legacy systems), intricate approval workflows, and compliance requirements.

  • Steps: Extensive discovery, deep integration development (potentially custom APIs), sophisticated workflow design, comprehensive User Acceptance Testing (UAT), and large-scale change management.
  • Benefit: Transforms entire departments or business units for maximum long-term efficiency and strategic advantage.

Factors Influencing Timeline:

• Document Variety & Complexity: More varied layouts, lower quality scans, or presence of handwriting increase training/refinement time.
• Integration Needs: Legacy systems or multiple complex integrations add duration.
• Workflow Complexity: Intricate approval rules and exception handling require more configuration.
• Internal Resources: Availability of dedicated project managers, IT support, and business subject matter experts (SMEs).

Cloud-based IDP solutions like Nanonets streamline implementation significantly due to their no-code/low-code platforms, pre-trained models, and managed infrastructure, making them operational much faster than traditional software.

What about documents with tables or complex layouts?

This is where modern Intelligent Document Processing (IDP) solutions particularly excel, demonstrating a significant advantage over traditional OCR. Advanced IDP platforms like Nanonets are specifically designed to accurately extract data from documents containing tables or complex layouts.

Here's how they handle these challenges:

• Tables:

• Traditional OCR Limitation: Often struggles with tables, especially those without visible borders, with merged cells, or where text wraps onto multiple lines within a single cell. It tends to output flattened, unusable text or misinterpret row/column structures.

• IDP Solution: Advanced IDP platforms use specialized Computer Vision and Machine Learning (ML) models. These models are trained on vast datasets of diverse tables to:

• Intelligently Detect Table Boundaries: Accurately identify where a table starts and ends on a page, even if it's embedded within other text or lacks clear lines.

  • Understand Row & Column Structures: Precisely delineate rows and columns, correctly handling merged cells, multi-line entries, and varying column widths.
  • Extract Data with Context: Pull individual cell values while maintaining their logical relationship to column headers and rows, ensuring data integrity.
  • Handle Multi-page Tables: Seamlessly extract and consolidate data from tables that span across multiple pages.
  • Nanonets is renowned for its highly accurate table extraction, turning complex visual tables into structured data ready for spreadsheets or databases.

• Complex Layouts (e.g., Varied Forms, Unstructured Reports):

• Traditional OCR Limitation: Highly dependent on rigid templates. Any deviation in layout (e.g., a field moving, different fonts) breaks the template, requiring constant manual updates.

• IDP Solution: IDP platforms are layout agnostic. Their AI models learn the semantic meaning and contextual relationships of data fields, rather than relying on their fixed positions. They use ML and Natural Language Processing (NLP) to:

• Identify Fields by Context: Recognize an "invoice number" or a "patient ID" regardless of where it appears on the document, or how it's formatted.

  • Adapt to Variations: Accurately extract data from diverse invoice designs, varied application forms, or different report styles, even if they have never seen that specific layout before.
  • Process Unstructured/Semi-structured Data: Extract entities, categorize information, and understand key insights from free-form text blocks within reports or contracts.

This capability is essential for documents like invoices with complex line items, financial statements, various application forms, and scientific reports, where data is often presented in intricate visual structures.

Does IDP require constant maintenance?

While IDP (Intelligent Document Processing) isn't a "set and forget" technology, modern AI-powered systems require significantly less maintenance than traditional template-based OCR approaches. The nature of maintenance shifts from reactive template fixing to proactive system optimization and refinement.

Here’s a comparison and what constitutes typical maintenance:

• Traditional OCR (High Maintenance):

• Problem: Relied on rigid, manually defined templates. Any change in a document's layout (e.g., a new vendor invoice format, an updated government form) would immediately "break" the template.

  • Maintenance: Required constant, manual effort to re-create or re-configure templates. This was a tedious, costly, and time-consuming process.

• AI-Powered IDP (Lower, Strategic Maintenance):

• How it works: Modern IDP platforms like Nanonets are layout agnostic and use adaptive learning. The AI learns from data patterns, not fixed templates.

  • Reduced Manual Fixes: The system's ability to adapt to varying layouts means you don't need to manually update templates for every change.

• Primary Maintenance Activities (Optimization & Improvement):

• Human-in-the-Loop (HITL) Review: The most common "maintenance" activity is the ongoing review and correction of exceptions (documents where the AI has low confidence or failed a validation rule). These corrections are crucial, as they serve as training feedback for the AI, continuously improving its accuracy and reducing future exceptions.

  • Periodic Model Retraining: While AI learns continuously, formal periodic retraining (e.g., every few months or when significant new document types are introduced) helps update the models with new patterns and ensure optimal performance. This is often automated or simplified by the vendor.
  • Fine-tuning for New Document Types: When a completely new document type is introduced (e.g., a new type of compliance report), initial training (e.g., showing Nanonets a few samples) is needed, but once trained, it adapts.
  • Updating Integration Points: If connected systems (ERP, CRM) undergo major API changes, integration points might need adjustments.
  • Monitoring Performance: Regularly checking accuracy rates, Straight-Through Processing (STP) rates, and exception queues to identify areas for optimization.

• Cloud-Based Advantage:

• Managed Services: Cloud-based IDP solutions handle much of the technical maintenance automatically. The vendor manages security updates, performance optimizations, infrastructure scaling, and core platform updates. This offloads significant IT burden from the client.

In summary, while IDP isn't entirely "set and forget," it transforms maintenance from a reactive, manual, template-fixing burden to a proactive process of monitoring and guiding the AI's continuous learning, leading to increasingly efficient and accurate automation over time.

What should I look for in an IDP vendor's support offerings?

The quality and scope of an IDP vendor's support offerings are crucial for successful implementation, ongoing operation, and realizing the full value of your automation investment. Comprehensive support can significantly impact your team's adoption and the system's performance.

Key support elements to evaluate in an IDP vendor:

• Implementation Assistance & Onboarding:

• Initial Setup: Does the vendor provide hands-on help with the initial setup, platform configuration, and integration with your existing systems (ERP, CRM)?

  • Data Migration: Assistance with migrating any historical document data into the platform.
  • Dedicated Project Manager: For complex deployments, a dedicated project manager can guide you.

• AI Model Training & Optimization Support:

• Initial Training Guidance: Does the vendor provide expert guidance on how to effectively train the AI models for your specific documents (e.g., best practices for providing sample documents, highlighting fields in Nanonets' UI)?

  • Performance Tuning: Assistance with fine-tuning the AI models for optimal accuracy and Straight-Through Processing (STP) rates.
  • Adaptive Learning Support: Understanding how to leverage the human-in-the-loop (HITL) feedback mechanism to continuously improve AI performance.

• Training Programs:

• Role-Based Training: Tailored training modules for different user roles (e.g., administrators, AP clerks who do HITL, approvers).

  • Hands-on Practice: Opportunities for users to practice with real documents.
  • Ongoing Training: Support for new features and updates.

• Technical Support & Troubleshooting:

• Availability: What are the support hours (e.g., 24/7, business hours)? What channels are available (phone, email, chat)?

  • Response Time Guarantees (SLAs): What are the guaranteed response times for critical issues?
  • Issue Resolution: How quickly and effectively are technical issues resolved?
  • Troubleshooting Tools: Access to logs, dashboards, or self-service troubleshooting guides.

• Documentation & Knowledge Base:

• Comprehensive Docs: Clear, well-organized documentation for API usage, feature guides, and troubleshooting.

  • Knowledge Base/FAQs: A searchable repository of common questions and solutions.
  • User Community/Forums: A platform for users to share knowledge and get help from peers.
  • Nanonets typically offers extensive documentation, tutorials, and dedicated support.

• Customer Success Management:

• Dedicated CSM: For enterprise plans, a dedicated Customer Success Manager who acts as a strategic partner, helping you maximize ROI, identify new automation opportunities, and manage ongoing success.

  • Regular Reviews: Scheduled meetings to review performance, adoption, and strategic alignment.

• System Updates & Enhancements:

• Release Notes: Clear communication about new features, bug fixes, and updates.

  • Impact Assessment: Information on how updates might affect your existing workflows.

Evaluating these support offerings ensures you partner with a vendor that provides not just the technology but also the expertise and assistance needed for a successful and evolving automation journey.

How to evaluate IDP software for your business?

Evaluating Intelligent Document Processing (IDP) software for your business requires a strategic approach beyond just feature lists. Focus on factors that align with your specific document types, operational needs, and long-term goals.

Key evaluation criteria:

• Document Versatility & AI Accuracy:

• Can it truly handle your diverse documents (invoices, contracts, forms, reports)?

  • How accurately does it extract data from complex layouts, scanned images, and handwriting? Seek high straight-through processing (STP) rates. Nanonets excels in this, with AI trained for layout-agnostic extraction across various documents.
  • Does it offer domain-specific pre-trained models relevant to your industry (e.g., finance, logistics, HR)?

• Ease of Customization & Adaptive Learning:

• Is it template-free? Can you easily train the AI for your unique document layouts with minimal samples (e.g., in a no-code UI like Nanonets' model builder)?

  • Does it feature Human-in-the-Loop (HITL) that learns from corrections, continuously improving accuracy over time for your specific document variations?

• Integration Capabilities:

• How seamlessly does it integrate with your existing core systems (ERP, CRM, DMS, accounting software)? Look for robust APIs, pre-built connectors, and iPaaS compatibility.

  • Can it both push extracted data and pull validation data?

• Workflow Automation Features:

• Does it just extract, or can it classify documents, auto-route for approvals, and trigger downstream actions based on extracted data?

  • How flexible is its workflow builder (e.g., for complex approval rules)?

• Scalability & Performance:

• Can it handle your current and projected document volumes efficiently, including peak loads?

  • What's the processing speed (latency and throughput)?

• Security & Compliance:

• Does it offer end-to-end encryption, robust access controls, and transparent data retention?

  • Does the vendor comply with relevant regulations (GDPR, SOC 2, HIPAA, ISO 27001)? Nanonets prioritizes these.

• Total Cost of Ownership (TCO) & ROI:

• Beyond license fees, consider implementation costs, maintenance, and the potential ROI from labor savings, error reduction, and faster processes.

• Vendor Support & Expertise:

• Evaluate implementation assistance, training, technical support, and ongoing customer success management.

A thorough evaluation ensures you select an IDP solution that truly transforms your document-heavy processes.

Use cases of IDP in logistics, finance, and HR?

Intelligent Document Processing (IDP) is a versatile technology automating complex, document-heavy workflows across numerous industries by extracting, classifying, and validating data from diverse formats.

• Logistics & Supply Chain:

• Purpose: Streamline inbound/outbound operations, improve tracking, and enhance inventory accuracy.

  • Use Cases: Automated Goods Receiving (extracting data from Delivery Notes, Packing Slips, and GRNs to update WMS/inventory), Shipment Processing (extracting details from Bills of Lading, Air Waybills, and Manifests to update TMS/customs), and Vendor Invoice Matching (automating 2-way/3-way matching of invoices against POs/GRNs).
  • Benefit: Faster cargo flow, reduced manual errors in inventory, improved supply chain visibility. Nanonets excels in logistics document processing.

• Finance & Accounting:

• Purpose: Accelerate financial close, enhance accuracy, ensure compliance, and optimize cash flow.

  • Use Cases: Accounts Payable (AP) Automation (extracting data from Invoices, Purchase Orders, and Expense Reports for automated matching, approval workflows, and direct posting to ERPs), Bank/Credit Card Reconciliation (extracting transaction details from Bank Statements and Credit Card Statements to automate matching), and Loan/Mortgage Processing (automating data extraction from Loan Applications, Income Statements, and KYC documents).
  • Benefit: Significant cost savings, faster processing cycles, improved audit readiness, real-time financial insights. Nanonets is a prominent IDP for finance and AP.

• Human Resources (HR):

• Purpose: Streamline employee lifecycle management, improve HR data accuracy, and ensure compliance.

  • Use Cases: Employee Onboarding (extracting data from Employee Application Forms, Offer Letters, I-9s, W-4s, and contracts to automatically populate HR Information Systems - HRIS), Resume Screening (automatically parsing Resumes to extract skills, experience), and Incident/Claim Reporting (extracting details from Employee Incident Reports, Workers' Compensation Claims).
  • Benefit: Faster hiring, reduced administrative burden, improved employee data accuracy, enhanced compliance.

In all these sectors, Nanonets' AI-powered IDP is crucial for turning unstructured document data into actionable information, enabling end-to-end automation.

How do IDP tools handle handwritten documents or poor-quality scans?

Modern IDP (Intelligent Document Processing) tools are specifically designed to handle challenging inputs like handwritten documents or poor-quality scans, significantly surpassing traditional OCR capabilities. They achieve this through advanced Artificial Intelligence (AI) and Machine Learning (ML) techniques.

• Handwritten Documents/Entries:

• How it works: IDP platforms integrate specialized Handwritten Text Recognition (HTR) engines, a form of advanced AI. Unlike basic OCR, HTR models are trained on vast datasets of diverse handwriting styles, allowing them to interpret a wide range of legible handwriting, from clear print-style to more challenging cursive.

  • Accuracy: Accuracy varies based on legibility (e.g., 80-90% for clear print-style; 50-70% for messy cursive).
  • Nanonets has robust HTR capabilities to extract handwritten data from various forms and documents.

• Poor-Quality Scans / Noisy Images:

• How it works: IDP solutions incorporate advanced Image Pre-processing techniques before OCR. These include de-skewing/rotation, de-noising/de-speckling, binarization/contrast enhancement, and glare/shadow reduction. These steps significantly clarify the image, making the text much more readable for the OCR engine, even if the original scan was poor.

  • Advanced OCR Engine: After pre-processing, the cleaned image is fed into a highly sophisticated AI-powered OCR engine. This engine uses deep learning models specifically trained on noisy or distorted text. It can intelligently infer characters even if they are partially obscured or blurry, leveraging context.

• Human-in-the-Loop (HITL):

• Crucial for Assurance: For critical data, even the best AI will have uncertainties with very challenging inputs. IDP platforms route these "low-confidence" extractions for human review and correction.

  • Adaptive Learning: Human corrections within the HITL process (as supported by Nanonets) feed back into the AI model, continuously improving its accuracy for similar challenging documents in the future.

By combining robust image pre-processing with advanced AI (HTR, deep learning OCR) and intelligent human oversight, IDP tools effectively transform difficult-to-read documents into accurate, usable data, vastly expanding automation possibilities.

How do IDP platforms learn from corrections and improve over time?

The ability of IDP platforms to learn from corrections and continuously improve over time is a core differentiator of Artificial Intelligence (AI)-powered solutions. This "adaptive learning" mechanism is fundamental to achieving high, sustained accuracy and increasing Straight-Through Processing (STP) rates.

Here's how it works:

• Initial Model Training: IDP platforms (like Nanonets) come with pre-trained AI models that understand common document types (e.g., invoices, resumes, contracts). For unique documents, initial custom training involves showing the AI a few sample documents and highlighting the data fields to extract.

• Automated Extraction & Confidence Scores: When a new document is processed, the AI model attempts to extract data. For each extracted field, the AI generates a confidence score, indicating how certain it is about the accuracy of its extraction.

• Human-in-the-Loop (HITL) Feedback Loop:

• Identification of Exceptions: Documents (or specific fields) are automatically flagged for human review if the AI's confidence score falls below a predefined threshold, the extracted data fails a validation rule, or the document type is new or highly ambiguous.

  • Human Correction: A human operator reviews the flagged document in a user-friendly interface (like Nanonets' validation screen), comparing the extracted data against the original document. They then correct any inaccuracies or ambiguities.
  • Adaptive Learning & Model Refinement: Crucially, every time a human makes a correction during the HITL process, that specific feedback is fed back into the AI's underlying Machine Learning (ML) model. The ML model uses this new, corrected data to refine its algorithms, adjusting its internal "understanding" of patterns, layouts, and contexts.

• Continuous Improvement: This iterative feedback loop means the AI continuously gets smarter and more accurate for future documents, especially for those specific to your business or your challenging document types. The exception rate decreases over time, leading to higher STP.

This process ensures that the IDP system (e.g., Nanonets' AI) is not static; it's a dynamic, self-improving engine that becomes increasingly efficient and accurate the more it processes your real-world documents and receives your expert feedback.

How does IDP integrate with RPA, CRM, and ERP tools?

Intelligent Document Processing (IDP) integrates deeply and seamlessly with Robotic Process Automation (RPA), Customer Relationship Management (CRM), and Enterprise Resource Planning (ERP) tools to create end-to-end automated workflows, ensuring data flows efficiently across an organization's tech stack.

• Integration with RPA (Robotic Process Automation):

• Role: IDP provides the "brain" (document understanding) that RPA bots often lack. RPA acts as the "hands" that execute tasks.

  • How it works: IDP (e.g., Nanonets) extracts structured data from complex documents (invoices, contracts, forms). This structured data is then fed to an RPA bot (e.g., UiPath, Automation Anywhere, Blue Prism). The RPA bot uses this clean data to interact with other systems (e.g., logging into a legacy ERP, entering invoice data into a specific field, creating a customer record in an old CRM that lacks APIs).
  • Benefit: Unlocks automation for processes previously stuck due to unstructured data input or reliance on legacy systems with no APIs. RPA handles the system interaction, while IDP handles the document intelligence.

• Integration with CRM (Customer Relationship Management):

• Role: Enrich customer profiles, automate lead management, streamline onboarding.

  • How it works: IDP extracts data from customer-related documents (e.g., lead forms, scanned business cards, customer inquiry emails, KYC documents, contracts). This data (e.g., contact info, product interests, contract terms) is then automatically pushed into CRM systems (e.g., Salesforce, HubSpot, Microsoft Dynamics 365) via APIs or pre-built connectors. Nanonets offers direct connectors for popular CRMs.
  • Benefit: Ensures customer profiles are accurate and up-to-date, speeds up lead qualification, automates onboarding, and provides sales/marketing teams with timely insights.

• Integration with ERP (Enterprise Resource Planning) Systems:

• Role: Automate core financial and operational processes (e.g., Accounts Payable, General Ledger posting, inventory management, order processing).

  • How it works: IDP extracts structured data from financial documents (e.g., invoices, purchase orders, delivery notes, expense reports, bank statements). This data is then automatically pushed into ERP systems (e.g., SAP, Oracle, NetSuite, Microsoft Dynamics 365, Sage) via robust APIs (e.g., NetSuite SuiteTalk) or pre-built connectors. Nanonets provides strong, direct integrations with major ERPs, facilitating real-time data flow for AP, procurement, and GL.
  • Benefit: Automates tasks like vendor bill creation, 2-way/3-way matching, GL coding, inventory updates, and financial reconciliation, leading to significant cost savings and faster financial closes.

Common Integration Methods:

• APIs (RESTful/SOAP): The most common and robust method for real-time, bidirectional data exchange.
• Pre-built Connectors: Offered by IDP vendors for popular systems, simplifying setup.
• iPaaS (Integration Platform as a Service): Middleware tools (e.g., Workato, Zapier, Make) connect IDP to various systems, enabling complex workflow orchestration.
• File-based Exchange: CSV, XML, JSON files transferred via SFTP or cloud storage (for batch processing or legacy systems).

This deep integration allows IDP to be a transformative layer, unlocking data from documents and fueling end-to-end automation across the enterprise.

Which IDP tools help automate contract data extraction?

Automating contract data extraction is a highly specialized application of IDP, crucial for legal, finance, sales, and procurement departments to manage obligations, assess risks, and track key terms. Various IDP tools excel here, leveraging Natural Language Processing (NLP) specifically for legal text.

Leading IDP tools that help automate contract data extraction include:

• Nanonets: A prominent AI-driven Intelligent Document Processing (IDP) platform with strong capabilities for contract data extraction. Nanonets excels by:

• Advanced AI-OCR & NLP: Accurately extracting key clauses, terms, and parties from contracts (PDF, DOCX, scanned images), even when legal language is nuanced or layouts are complex. It understands the context of legal phrasing.

  • Layout Agnosticism: Adapting to diverse contract templates (e.g., NDAs, sales agreements, vendor contracts) without requiring fixed templates for each.
  • Custom Model Training: Allowing users to easily train AI models for unique, company-specific contract clauses or data points by simply highlighting examples in a no-code UI.

• Table Extraction: Precisely extracting tabular data within contracts (e.g., pricing schedules, service level agreements, product lists).

  • Integration: Providing APIs for seamless integration with Contract Lifecycle Management (CLM) systems, ERPs, CRM, and legal platforms.

• ThoughtTrace: Specialized in AI-powered contract analytics. Uses advanced NLP and ML to identify, extract, and organize specific clauses, obligations, and risks from complex legal documents. Strong for legal compliance and risk management.

• DocuSign CLM (Contract Lifecycle Management) with AI: Integrates AI capabilities into its CLM platform. It can extract key terms, identify clauses, and streamline contract review and negotiation within the CLM ecosystem.

• Conga: Offers CLM solutions with AI features for contract analysis, enabling extraction of key data points and clauses to improve contract visibility and management.

• ABBYY FlexiCapture / Vantage: While general IDP platforms, their strong OCR and customizable AI/NLP capabilities make them effective for large-scale contract data extraction projects, especially when configured by experienced integrators.

• Google Cloud Document AI (with Contract Processor): Google offers a specialized "Contract Processor" that leverages its vast AI research to extract common entities and clauses from various contract types, returning structured JSON. Requires developer effort for full workflow integration.

• Microsoft Azure AI Document Intelligence (with custom models): Can be trained with custom models to extract specific data from contracts, leveraging its powerful OCR and intelligent processing.

When choosing an IDP tool for contract data extraction, consider the complexity and volume of your contracts, the specificity of the clauses you need to extract (general terms vs. highly nuanced legal language), the required accuracy, and how seamlessly it integrates with your existing legal or contract management systems.

businesses love us

Don’t take our word for it. See what others have to say

Dennis Elder

Director of Product, PayGround

“There was a visible difference in how the app worked, and we were able to appeal to our customers by making it easy to pay bills”

Read Story ->

Kale Flaspohler

Financial Advisor, ProPartners Wealth

“We are seeing a major difference in accuracy, as Nanonets provides a >95% accuracy which has helped cut down our processing time by ~50%.”

Read Story ->

Catherine Gallagher

Accounts Payable, SaltPay

“Nanonets' direct integration with SAP helped SaltPay automate a crucial part of their Accounts Payable process”

Read Story ->

Luke Faulkner

Product Manager, Tapi

“Tapi has been able to save 70% on invoicing costs, improve customer experience by turnaround of seconds from >6hrs and free up staff members from tedious work”

Read Story ->

Ryan Hess

Head of Accounts Payable, ACM

"I have built a relationship with Nanonets which is an important ideal of ACM and it feels now as if they are part of the family."

Read Story ->

Tay Kim

Product Operations Manager, Expatrio

"A great product and amazing customer support. Their response time was amazing. They went an extra mile to figure a plan that helps us scale our business."

Read Story ->

Dennis Elder

Director of Product, PayGround

“There was a visible difference in how the app worked, and we were able to appeal to our customers by making it easy to pay bills”

Read Story ->

Kale Flaspohler

Financial Advisor, ProPartners Wealth

“We are seeing a major difference in accuracy, as Nanonets provides a >95% accuracy which has helped cut down our processing time by ~50%.”

Read Story ->

Catherine Gallagher

Accounts Payable, SaltPay

“Nanonets' direct integration with SAP helped SaltPay automate a crucial part of their Accounts Payable process”

Read Story ->

Luke Faulkner

Product Manager, Tapi

Read Story ->

Ryan Hess

Head of Accounts Payable, ACM

"I have built a relationship with Nanonets which is an important ideal of ACM and it feels now as if they are part of the family."

Read Story ->

Tay Kim

Product Operations Manager, Expatrio

"A great product and amazing customer support. Their response time was amazing. They went an extra mile to figure a plan that helps us scale our business."

Read Story ->

Dennis Elder

Director of Product, PayGround

“There was a visible difference in how the app worked, and we were able to appeal to our customers by making it easy to pay bills”

Read Story ->

Kale Flaspohler

Financial Advisor, ProPartners Wealth

“We are seeing a major difference in accuracy, as Nanonets provides a >95% accuracy which has helped cut down our processing time by ~50%.”

Read Story ->

Catherine Gallagher

Accounts Payable, SaltPay

“Nanonets' direct integration with SAP helped SaltPay automate a crucial part of their Accounts Payable process”

Read Story ->

Luke Faulkner

Product Manager, Tapi

Read Story ->

Ryan Hess

Head of Accounts Payable, ACM

"I have built a relationship with Nanonets which is an important ideal of ACM and it feels now as if they are part of the family."

Read Story ->

Tay Kim

Product Operations Manager, Expatrio

"A great product and amazing customer support. Their response time was amazing. They went an extra mile to figure a plan that helps us scale our business."

Read Story ->

4.9 Rating on Capterra

High performer Summer

2024 by G2 Crowd

\ Users Love Us on G2](https://www.g2.com/products/nanonets/reviews?utm_source=rewards-badge)

4.9 Rating on GetApp

Sign up to explore financial document types with Nanonets

No credit card needed

$200 worth of free credits

Unlimited time on Free plan

Sign up for free