Automation
AI + Automation
Bank
Insurance
Logistics
近年来，每当人们提起人工智能代理，许多人的第一反应仍然是“问答助手”：你问它问题，它就给你答案；你让它写点什么，它就为你生成一段文字。更进一步来说，这可能会让人想起不久前流行的个人智能助手——那种可以“帮你订购小龙虾”或“帮你操作电脑”的助手。
听起来很酷，但当应用于企业环境时，新的问题会立即出现：数据安全能否得到保障？权限是否过于宽泛？执行过程能否得到控制？它能否正确执行指令？如果出现问题，能否追踪到问题所在？
这些担忧并非空穴来风。企业真正需要的并非仅仅是“看起来很智能”的工具，而是一个能够按照规则稳定工作、与系统连接、留下操作记录并得到妥善管理的数字化员工。
这正是i-Search旨在解决的问题。
很多人可能会问：文档识别不是OCR工具已经能做到的吗？的确，如果任务仅仅是从图像中识别文本，很多工具都能做到。但企业级文档处理远不止“清晰地读取文字”那么简单。真正的挑战在于：能否自动获取和分类文档？识别出的数据是否需要验证？是否需要转换格式？是否需要拆分数据？是否需要将结果写入ERP系统？以及工作流程是否需要自动触发审批？
这些正是 i-Search DocAgent 的设计初衷。
财务文件处理
难点从来不仅仅在于“看懂文字”。
在企业日常运营中，财务文件处理看似是一项不起眼的例行任务，但往往会耗费大量人力。
对于财务部门而言，付款申请表绝不仅仅是一份单独的文件。它背后是一系列流程，包括费用审核、应付账款、对账管理以及后续的付款执行。
某集团财务部门每月需要处理大量的付款申请表、发票、费用明细及相关附件。文件数量众多，格式各异，信息详尽。人工审核每个文件、逐项核对很容易耗费大量时间。
在应付账款流程中，这一点尤为突出，因为付款前的单据准备往往是最耗时的步骤。财务人员需要从电子邮件、文件夹或客户平台收集付款申请表、发票和附件，然后核实金额、供应商、账户和费用明细，最后将所有内容整理成标准格式。
The problem is that documents submitted by different clients are often not standardized. Some are PDFs, some are scanned files, while others may be images, spreadsheets, or email attachments. Staff need to repeatedly open files, search for fields, copy and paste information, split amounts, and then enter the data into Excel or business systems. Although the process may look like simple “document handling,” it actually requires experience, takes a lot of time, and is prone to errors such as missed information, incorrect entries, or repeated input. These mistakes can affect later reconciliation and payment accuracy, as well as payment timelines and amount management.
Therefore, what enterprises need is no longer just a tool that can “recognize text,” but a process capability that can continue pushing document processing forward.
What Exactly Does DocAgent Do?
Let Documents Enter the Workflow on Their Own
After introducing DocAgent, the document processing steps that were previously scattered across manual operations are connected into a smoother automated workflow.
In this financial scenario, DocAgent can first automatically obtain payment application forms, invoices, expense sheets, and other documents from channels such as email inboxes and folders. It can identify document types and extract key fields such as supplier, amount, date, document number, account, and expense item. If there are multiple invoices, multiple expense details, or several amounts contained in one document, the system can also split, classify, and organize the information according to preset rules.
More importantly, DocAgent does not stop after “recognition is complete.” The extracted data can continue to go through verification and format conversion, and then be written into ERP systems, submitted for approval, or used to trigger follow-up workflows based on business rules. When the data is normal, the process moves forward automatically; when abnormal information appears, the system prompts staff to focus on manual confirmation.
In this way, finance staff no longer need to keep following documents step by step, nor spend large amounts of time on “finding fields, copying and pasting, and organizing spreadsheets.” Instead, they can focus their energy on exception judgment, complex business handling, and client communication.
Therefore, the change brought by DocAgent is not just about reducing the number of fields to be entered manually. It is about making back-office workflows truly move. Problems that used to be solved by piling on manpower are gradually becoming digital capabilities that are replicable, traceable, and continuously optimizable.
Where There Are Many Documents and Detailed Rules
DocAgent May Be Needed
Accounts payable is only the beginning.
In many enterprise scenarios, documents do not exist in isolation. Orders, logistics documents, customs clearance files, contracts, quality inspection reports — behind these documents, there are often fixed workflows, as well as a large amount of repetitive data entry, verification, and circulation work.
Order management scenario: DocAgent can receive and process orders from channels such as emails, EDI, portals, and mobile applications. It can automatically complete data extraction, verification, and standardization, and connect with systems such as ERP, CRM, and WMS, helping enterprises reduce manual entry errors and accelerate order confirmation, fulfillment, and delivery.
Transportation and logistics scenario: DocAgent can process logistics documents such as invoices, CMR consignment notes, bills of lading, packing lists, and freight invoices. It can automatically extract information such as weight, dimensions, distance, and destination, while supporting the recognition of multilingual documents, scanned files, photographed documents, and handwritten content. This helps logistics teams reduce manual entry and speed up document processing.
Customs clearance scenario: DocAgent can process customs invoices, bills of lading, packing lists, delivery notes, and other customs clearance documents. It supports data extraction, email drafting, and file splitting, helping enterprises reduce manual sorting and communication costs, improve customs clearance efficiency, and shorten subsequent billing cycles.
Although these scenarios may look different, the underlying problems are similar: too many documents, mixed formats, detailed rules, and heavy pressure on manual processing. What DocAgent aims to do is connect these steps, which were previously scattered across manual operations, into business workflows that can run automatically.
The Next Step for AI Agents
Is to Enter Real Enterprise Workflows
At this point, we can return to the original question:
What is the difference between DocAgent and ordinary OCR tools?
OCR solves the problem of “seeing the text.”
DocAgent solves the problem of “what to do after seeing it.”
In the AI era, what enterprises truly expect from technology upgrades is not just smarter systems, nor tools that can generate a few more pieces of content. Rather, they expect technology to begin taking on real work. Information in documents can be automatically obtained, recognized, extracted, verified, written into systems, and then continue to drive processes such as approvals, reconciliation, and payment.
This is where enterprise-level Agents truly belong.
对于员工而言，它可以接管重复、繁琐且容易出错的工作部分。对于企业而言，它可以带来更稳定、可控且可追溯的流程能力。数据安全、权限控制、规则执行和结果跟踪——这些个人人工智能工具经常出现的问题，正是企业级 DocAgent 旨在解决的问题。
人工智能代理不再仅仅能够聊天或提供建议。它们正在进入企业的实际运营中，并开始帮助员工完成工作。
未来，随着i-Search DocAgent技术能力的不断提升，更多业务场景将从“人工处理”转向“智能执行”。无论是费用报销、发票核对、合同归档、对账、付款还是审批流程流转，这些功能都将成为企业提升财务运营效率的重要支撑。
