Monitor and manage data assets and processes
Allocate tasks to automation workers and monitor them. Also, it feeds processes with data, credentials, files provided by process owners.
Monitor and manage data assets and processes
Allocate tasks to automation workers and monitor them. Also, it feeds processes with data, credentials, files provided by process owners.
Monitor and manage data assets and processes
Allocate tasks to automation workers and monitor them. Also, it feeds processes with data, credentials, files provided by process owners.
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.
Automation Commander
Build end-to-end automation with AI-powered low-code tool
Orchestrate and monitor automation components to strengthen governance
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All process data can be recorded in logs and roles can be allocated to suitable person, ensuring compliant and auditable automation.
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You can securely as well as conveniently manage all data assets alongside the automation process with the help of our powerful access control and encryption mechanisms .
Difference between types
floating authorization
binding machine
foating authorization
Difference between community and enterpise versions
Paradigm Shift in RPA Development
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Unified platform for managing all workflows or data
Provide standardized management for workflows, robots, data, and tasks, enabling enterprises to operate with greater precision and efficiency. It supports fine-grained authorization, allowing workflows to be assigned to specific departments or individuals for more controlled access. Different workflow versions can be maintained to adapt to evolving environments, with centralized deployment ensuring the right version is delivered without the need for manual updates. Users can define custom parameters to control how workflows run, ensuring alignment with specific business needs. In addition, execution histories and logs are easily accessible, making it simple to trace and troubleshoot workflow operations.
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Create, manage, and orchestrate multiple tasks with flexibility
Quickly create multiple tasks with one click. Tasks are automatically assigned across available robots and executed based on defined priority levels—ensuring urgent tasks are handled promptly. Tasks can be reassigned or re-executed as needed, enabling flexible responses to exceptions in business operations. Monitor task status in real time and access detailed execution logs for full visibility into each task’s performance and outcome.
Role-based permission control
Permissions are fully managed across workflows, bots, data, credentials, files, and tasks. Employees are grouped by roles, with each role configured to access only the relevant functions and operations. Business data permissions can be independently assigned based on employee responsibilities, ensuring data privacy and enabling more vertical task specialization. The platform also includes a set of prebuilt business roles tailored to common scenarios, helping enterprises streamline role-based permission setup and reduce configuration complexity.
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.