Intelligent Document Processing: unlock value of enterprise data
Powered by advanced OCR, NLP, and LLMs, Laiye IDP delivers six core capabilities to intelligently parse hundreds of document types. Unstructured data are utilized to boost decision-making efficiency by 10 times.
Intelligent Document Processing: unlock value of enterprise data
Powered by advanced OCR, NLP, and LLMs, Laiye IDP delivers six core capabilities to intelligently parse hundreds of document types. Unstructured data are utilized to boost decision-making efficiency by 10 times.
Intelligent Document Processing: unlock value of enterprise data
Powered by advanced OCR, NLP, and LLMs, Laiye IDP delivers six core capabilities to intelligently parse hundreds of document types. Unstructured data are utilized to boost decision-making efficiency by 10 times.
What is robotic process automation (RPA)?
The evolution of AI, especially large language model (LLMs) significantly influnces the development of RPA. Besides serving the as the executing role, AI can offer the orchestration, governance, and security assurance for deployment of enterprise-grade intelligent automation.
What is intelligent document processing (IDP)?
Six Core Capabilities of IDP
Core advantages
Leading performance
Powered by dual engines, semantic understanding via LLMs and Laiye’s proprietary OCR, our solution delivers top-tier recognition accuracy, even in complex scenarios.
Enterprise-grade securityCertified by international standards such as ISO/IEC 27001, ensuring 99.9% system reliability and data security.
Agile and intelligent
Zero-shot document processing, with closed-loop collaboration across departments. Human feedback fuels continuous self-learning and iteration.
Open and integrated
Natively integrated with RPA. Seamless API/MCP connectivity with 50+ business systems and AI applications for end-to-end intelligent document handling.
Intelligent Document Processing
Empower your robot with capablities to process documents
Value in various scenarios
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Using IDP for invoice recognition (combined with RPA) for form filling not only avoids the 8% manual error rate, but also reduces the waiting process procession from 3 days to just 20 minutes.
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Traditional processing of English PO documents takes over 30 minutes per order in cross-border trades. IDP’s semantic parsing of overseas invoices boosts reconciliation efficiency by 200%.
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In legal and risk control, manual detection misses over 15% of contract tampering. IDP document comparison captures millimeter-level changes to avoid 27 risks annually.
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In medical archives, utilization of test report data is below 20%. IDP extracts key indicators in structured form, improving research data extraction efficiency by 10 times.
Difference between types
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Difference between community and enterpise versions
Paradigm Shift in RPA Development
Rich pre-built models
There are rich pre-built models to understand images and texts in life, such as form, invoice, captcha, stamp, and QR code. More than 10 languages including English, Chinese (traditional & simplified), Japanese, Korean, French, etc. are supported.
Data extraction with self-learning mode
Support a complete document processing workflow including data management, annotation, evaluation and version management . The model can automatically keep learning with data collected by the Collaboration Hub.
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Document classification in automation process
To extract information from invoices and purchase orders is a common automation request. Laiye provides the document classification model based on machine learning. Therefore, the agent can intelligently recognize different features of each document type, realizing truly end-to-end automation.
Differences between SaaS Version & On-premise Version
Differences among different types
Differences between community & enterprise versions
Differences between community version & enterprise version
RPA in the future
RPA will continue to be a core tool in enterprise digital transformation. By leveraging low-code platforms, it lowers the development barrier and enables cross-system task automation. Its non-intrusive nature allows for seamless integration with legacy systems, ensuring execution of complex workflows. Meanwhile, containerization drives its evolution toward cloud-native architecture, enhancing collaboration with API-based automation.
Digital Worker Builder: Enables graphical configuration of AI Agents with complex decision-making capabilities to support advanced automation scenarios.
Agent Interaction Hub: Integrates MCP technology to provide standardized system interfaces, allowing business users to directly trigger automation processes.
Cognitive Automation Upgrade: Combines large language models to enable natural language interaction, document comprehension, and intelligent decision-making.
Employee Empowerment: Frees human resources from repetitive tasks (e.g., monthly report preparation in finance accelerated by 40x), allowing focus on higher-value work.
Human-AI Collaboration: Enables natural language interaction through chatbots and smart forms, automatically triggering manual intervention when exceptions (like invoice issues) arise.
Democratized Automation Skills: Empowers business users to build automation workflows independently, with a low-code approach validated by an 800,000-strong developer community.
RPA center of excellence
The RPA center of excellence (CoE) is a cross-functional team that consolidates best practices in RPA deployment, standardizes data interfaces and operation models, and drives enterprise-wide automation at scale.
For the enterprise: Enhances operational decision-making, unlocks data value, and reduces the cost of repetitive tasks.
For employees: Empowers staff to develop RPA skills, boosting productivity and creativity.
IDP in the future
Driven by large language models, semantic understanding of documents evolves toward decision-making analysis, enabling a closed-loop decision cycle, from clause impact analysis, cost simulation, to actionable recommendations.
Enables joint analysis of text, images, and tables to generate actionable business insights. Breaking the limitations of single-modal processing, it builds an integrated analysis pipeline, from documents to seals, signatures, and data tables.
Connects with enterprise data asset management platforms to accelerate the structuring of data resources (“into the table”). Builds a value chain from document data, to asset valuation, and to business insights.