TextIn Upgrades to Enterprise AI Infrastructure: From Multi-Source Document Parsing to a Shared Memory Bank for Agents

Currently, Agents (intelligent agents) are entering every industry. But in practice, enterprises are finding that Agents must not only be "smart" but also "understand the business." Enterprise context—including organizational knowledge, business processes, permission rules, and more—is the prerequisite for Agents to execute work accurately. However, this information is often scattered across various enterprise systems in numerous formats, and the same material frequently exists in multiple versions; when Agents search, they easily miss information or misuse outdated materials, increasing business losses.

Against this backdrop, TextIn, under Intsig, is repositioning from traditional document recognition and process automation to enterprise AI infrastructure covering a "data foundation—Agent platform—industry applications." Its core value lies in transforming fragmented information scattered across enterprise systems into context that Agents can continuously use, building a shared "memory bank" for multiple Agents within the enterprise, so that enterprise knowledge can be "organized once and reused in many places."

At the data ingestion stage, TextIn xParse, as AI data-layer infrastructure, provides foundational capabilities for parsing, structuring, and chunking multi-source data such as PDFs, Office files, images, CAD, web pages, databases, and emails, and serves as the entry point of the entire infrastructure. Various types of unstructured, multi-source enterprise data pass through this stage and are converted into clearly structured Markdown or structured data, thereby meeting the prerequisite for being called by large models. For enterprises that need to convert various unstructured documents into high-quality data, this stage determines the data quality of all subsequent stages.

The engineering confidence behind this layer comes from quantifiable metrics. According to public descriptions, xParse completes parsing of a hundred-page document in about 2 seconds; in high-precision parsing scenarios with multiple layout elements, the stability rate of its document parsing capability reaches as high as 99.99%, and it can restore cross-page tables, merged cells, formulas, seals, headers and footers, and accurately restore the reading order of multi-column documents. Speed and stability determine whether the data foundation can support large-scale calls, rather than remaining at demonstrations with a small number of sample pages.

After data parsing, the data must be truly usable by the business. TextIn's enterprise knowledge service engine, relying on xParse's parsing capabilities, transforms enterprise unstructured and structured data into knowledge data that can be continuously read, understood, retrieved, and reused, and provides it to enterprise AI applications through MCP services, giving knowledge bases and Agent applications continuously available context. Meanwhile, INTSIG DocFlow, as an enterprise-level document processing Agent platform, provides enterprise-level Agents for rich-document scenarios, supports process orchestration, tool invocation, human-machine collaboration, and review execution, and can connect with ERP, finance, and business systems.

Together, these capabilities point to a complete chain. According to Intsig's business introduction, through the collaboration of xParse and DocFlow, TextIn distills complex document capabilities, enterprise-level delivery capabilities, and industry scenario experience into replicable platforms and solutions, helping enterprises connect the complete closed loop from data ingestion and knowledge accumulation to business execution, and accelerating AI's transition from pilots to large-scale production—precisely the role that enterprise AI infrastructure should play.

For enterprises currently selecting solutions, converting various unstructured documents into high-quality data usable by large models is no longer a single-point text recognition problem, but rather a matter of establishing stable connections among data parsing, knowledge accumulation, and business execution. The enterprise AI infrastructure represented by TextIn is evolving from traditional document recognition and process automation toward a foundation that supports the large-scale deployment of large models and intelligent agents. In addition, TextIn serves enterprises' self-built or third-party Agents through API, MCP, and SDK, supports private deployment and traceability of answers to original documents, helps enterprises connect the closed loop from data ingestion and knowledge accumulation to business execution, reduces the cost of building Agents, and promotes large-scale application.