Before an agent runs, Hancom Data Loader converts documents into data that AI can understand, separating and extracting content down to semantic units and providing it as agent-friendly metadata.<\/p>\n\n
Why is document datafication so difficult when adopting enterprise AI?<\/strong><\/h2>\n\nThis is because large language models (LLMs) cannot read internal documents such as PDF, HWP, and HWPX as-is.<\/p>\n\n
A common challenge faced by public institutions, enterprise DX teams, and SI partners considering the adoption of in-house AI assistants, RAG (Retrieval Augmented Generation), or agents is handling unstructured documents.<\/p>\n\n
Why Document Structuring Is Necessary: The Gap Between LLMs and Unstructured Documents<\/strong><\/strong><\/h3>\n\nEnterprises hold vast amounts of materials\u2014manuals, reports, contracts, and official documents in HWP\/HWPX\u2014but not many are in a form that LLMs can use directly. <\/p>\n\n
In PDFs, table, multi-column, and caption structures can be damaged, and in HWP and HWPX files, extracting only text does not convey meaning accurately. As table row and column order changes, answers can differ from the actual content. Only by converting documents into structured data can LLMs properly read internal information. <\/strong><\/p>\n\nWhy Document Parsing Quality Matters in a RAG Pipeline<\/strong><\/strong><\/h3>\n\nFor organizations pursuing AI transformation (AX), structured document data affects not only search but also the reliability of agent judgment and execution.<\/p>\n\n
In a RAG pipeline, document preprocessing quality is directly reflected in retrieval, decision-making, and execution results.<\/p>\n\n
If table structure information is lost during parsing, irrelevant text may be included, and during embedding, positions can be distorted and mapped incorrectly.<\/p>\n\n
<\/div>\n\n![\"An](\"https:\/\/blog.hancom.com\/wp-content\/uploads\/2026\/05\/hancom-data-loader-what-is-intro-1-1024x576.png\")
<\/figure>\n\n<\/div>\n\nDocument parsing solution: definition and core role of Hancom Data Loader<\/strong><\/strong><\/h2>\n\nHancom Data Loader: an automated document parsing solution<\/strong><\/h3>\n\nHancom Data Loader<\/a> is a document parsing solution developed by Hancom.<\/strong> It converts various document formats such as HWP, HWPX, PDF, and OOXML into structured data that AI can understand, and can be used to build RAG-based knowledge search systems, secure training data for Large Language Models (LLMs), and digitize corporate documents.<\/p>\n\nIt separates and extracts document semantic units\u2014including tables, titles, captions, and hierarchical structures\u2014along with OCR (Optical Character Recognition)\u2013based extraction, and provides them as agent-friendly metadata. As a document structure-preserving extraction method rather than general OCR, it is establishing itself as a core preprocessing technology for building RAG systems.
In addition, HWP and HWPX documents provide paragraph-based hierarchical structure information, and PDF AI supports visual information interpretation functions based on Image Captioning.<\/p>\n\n
(\u203b The image captioning function is currently in the PoC stage, and the commercial release schedule will be announced later.)<\/p>\n\n
<\/div>\n\nFrom Document Structure Analysis to Data Extraction<\/strong><\/h3>\n\nHancom Data Loader generates structured data through a three-step processing flow: document input \u2192 document analysis \u2192 data extraction<\/strong>. The extracted data can then be linked with RAG pipelines and LLMs to build AI services. <\/p>\n\nIn the document analysis stage, it runs two engines in parallel: rules-based analysis and AI-based document structure understanding. Structured documents with consistent formats are handled with rules, while unstructured documents that mix figures, tables, and images are analyzed precisely with AI. <\/p>\n\n
It provides, in a single processing flow, the core capabilities required for document preprocessing\u2014recognizing diverse document elements, detecting multi-column reading order, and extracting complex table and chart structures.<\/p>\n\n
The extracted structured data can be directly integrated with Hancom\u2019s proprietary RAG solution, HancomPedia, enabling rapid deployment of a document-based AI search system. <\/strong><\/p>\n\n<\/div>\n\n![\"A](\"https:\/\/blog.hancom.com\/wp-content\/uploads\/2026\/05\/hancom-data-loader-ai-document-processing-flow-1-1024x576.png\")
<\/figure>\n\n<\/div>\n\nDocument Data Extraction Targets: Support for Multiple Document Formats<\/strong><\/h3>\n\nCore Format Support for Public-Sector AI Adoption: HWP\/HWPX Parsing<\/strong><\/strong><\/h4>\n\nAs the government moves to expand HWPX usage from 2026 and gradually restrict HWP attachments<\/a>, full support for HWP and HWPX is becoming a prerequisite for public-sector AI adoption<\/strong>. Hancom Data Loader supports all HWP versions from 3.0 onward, and with a parser developed in-house by Hancom, it preserves a wide range of layout elements without loss<\/strong> without converting to PDF<\/strong>. <\/p>\n\nUniversal Support for All Document Formats \u2013 PDF and OOXML<\/strong><\/h4>\n\nPDF AI handles structure analysis and table structure restoration through DLA, TSR, and OCR, and minimizes data loss in scanned documents through OCR integration. OOXML extracts text from office documents using a text extraction method. <\/p>\n\n
Unstructured Visual Documents: PNG\/JPG<\/strong><\/h4>\n\nPNG and JPG recognize text and structure to datafy even non-digitized materials such as fax documents, scanned images, and photographed documents.<\/p>\n\n
\ud83d\udc49<\/strong>Go to Hancom Data Loader Live Demo<\/strong><\/a><\/p>\n\n<\/div>\n\nKey Features of Hancom Data Loader: DLA\/TSR\/OCR-Based Document Structure Analysis<\/strong><\/strong><\/strong><\/h2>\n\nDLA \u2013 AI-Based Structure Analysis That Extracts Metadata<\/strong><\/strong><\/strong><\/h3>\n\nHancom Data Loader’s DLA classifies objects into categories and extracts tables, footnotes, images, charts, captions, formulas, and even fonts as metadata.<\/p>\n\n
When metadata is extracted together, RAG retrieval accuracy improves, enabling policy search, administrative QA, and internal knowledge agents to use highly reliable data.<\/p>\n\n
Table\/Image\/Chart Structure Recognition: TSR Technology at the Core of Unstructured Data Processing<\/strong><\/strong><\/strong><\/h3>\n\nKey information in corporate documents is often concentrated in tables and charts rather than the main text. Based on TSR, Hancom Data Loader restores cell relationships\u2014including borderless tables, merged cells, and tables within tables\u2014to convert them into Markdown or HTML, and improves AI result quality by preserving multi-column layouts. <\/p>\n\n
Data Separation and Post-Editing: Data Loader Studio<\/strong><\/h3>\n\nFor documents where accuracy is difficult to ensure through automatic extraction alone, Data Loader Studio (an extended solution) is provided. Extraction results preprocessed by Data Loader can be reviewed side-by-side with the original document. Users can directly verify hierarchy, categories, reading order, and more, and post-correct necessary parts, enabling them to manage document structure and semantic relationships to fit actual business purposes. <\/p>\n\n
<\/div>\n\n![\"A](\"https:\/\/blog.hancom.com\/wp-content\/uploads\/2026\/05\/hancom-data-loader-usage-flow-1-1024x576.png\")
<\/figure>\n\n<\/div>\n\nHancom Data Loader for document-based AI<\/strong><\/h2>\n\n\ud83d\udda5\ufe0f<\/strong>Hancom Data Loader<\/strong><\/a><\/h3>\n\nHancom Data Loader<\/a> is a document parsing solution that converts HWP, HWPX, PDF, and OOXML into structured data<\/strong>. The extracted data can be integrated with Hancom\u2019s RAG solution, Hancompedia, enabling an end-to-end workflow\u2014from document collection to search and answers\u2014built on a single Hancom stack. Hancom Data Loader provides a document preprocessing environment that agents can trust. <\/p>\n\n\ud83d\udc49Go to Hancom Data Loader Live Demo<\/strong><\/a><\/p>\n\n\ud83d\udc49<\/strong>Inquire about Hancom Data Loader Implementation<\/strong><\/a><\/p>\n\n
\n\n
Enterprises hold vast amounts of materials\u2014manuals, reports, contracts, and official documents in HWP\/HWPX\u2014but not many are in a form that LLMs can use directly. <\/p>\n\n
In PDFs, table, multi-column, and caption structures can be damaged, and in HWP and HWPX files, extracting only text does not convey meaning accurately. As table row and column order changes, answers can differ from the actual content. Only by converting documents into structured data can LLMs properly read internal information. <\/strong><\/p>\n\n For organizations pursuing AI transformation (AX), structured document data affects not only search but also the reliability of agent judgment and execution.<\/p>\n\n In a RAG pipeline, document preprocessing quality is directly reflected in retrieval, decision-making, and execution results.<\/p>\n\n If table structure information is lost during parsing, irrelevant text may be included, and during embedding, positions can be distorted and mapped incorrectly.<\/p>\n\n Hancom Data Loader<\/a> is a document parsing solution developed by Hancom.<\/strong> It converts various document formats such as HWP, HWPX, PDF, and OOXML into structured data that AI can understand, and can be used to build RAG-based knowledge search systems, secure training data for Large Language Models (LLMs), and digitize corporate documents.<\/p>\n\n It separates and extracts document semantic units\u2014including tables, titles, captions, and hierarchical structures\u2014along with OCR (Optical Character Recognition)\u2013based extraction, and provides them as agent-friendly metadata. As a document structure-preserving extraction method rather than general OCR, it is establishing itself as a core preprocessing technology for building RAG systems. In addition, HWP and HWPX documents provide paragraph-based hierarchical structure information, and PDF AI supports visual information interpretation functions based on Image Captioning.<\/p>\n\n (\u203b The image captioning function is currently in the PoC stage, and the commercial release schedule will be announced later.)<\/p>\n\n Hancom Data Loader generates structured data through a three-step processing flow: document input \u2192 document analysis \u2192 data extraction<\/strong>. The extracted data can then be linked with RAG pipelines and LLMs to build AI services. <\/p>\n\n In the document analysis stage, it runs two engines in parallel: rules-based analysis and AI-based document structure understanding. Structured documents with consistent formats are handled with rules, while unstructured documents that mix figures, tables, and images are analyzed precisely with AI. <\/p>\n\n It provides, in a single processing flow, the core capabilities required for document preprocessing\u2014recognizing diverse document elements, detecting multi-column reading order, and extracting complex table and chart structures.<\/p>\n\n The extracted structured data can be directly integrated with Hancom\u2019s proprietary RAG solution, HancomPedia, enabling rapid deployment of a document-based AI search system. <\/strong><\/p>\n\n As the government moves to expand HWPX usage from 2026 and gradually restrict HWP attachments<\/a>, full support for HWP and HWPX is becoming a prerequisite for public-sector AI adoption<\/strong>. Hancom Data Loader supports all HWP versions from 3.0 onward, and with a parser developed in-house by Hancom, it preserves a wide range of layout elements without loss<\/strong> without converting to PDF<\/strong>. <\/p>\n\n PDF AI handles structure analysis and table structure restoration through DLA, TSR, and OCR, and minimizes data loss in scanned documents through OCR integration. OOXML extracts text from office documents using a text extraction method. <\/p>\n\n PNG and JPG recognize text and structure to datafy even non-digitized materials such as fax documents, scanned images, and photographed documents.<\/p>\n\n \ud83d\udc49<\/strong>Go to Hancom Data Loader Live Demo<\/strong><\/a><\/p>\n\n Hancom Data Loader’s DLA classifies objects into categories and extracts tables, footnotes, images, charts, captions, formulas, and even fonts as metadata.<\/p>\n\n When metadata is extracted together, RAG retrieval accuracy improves, enabling policy search, administrative QA, and internal knowledge agents to use highly reliable data.<\/p>\n\n Key information in corporate documents is often concentrated in tables and charts rather than the main text. Based on TSR, Hancom Data Loader restores cell relationships\u2014including borderless tables, merged cells, and tables within tables\u2014to convert them into Markdown or HTML, and improves AI result quality by preserving multi-column layouts. <\/p>\n\n For documents where accuracy is difficult to ensure through automatic extraction alone, Data Loader Studio (an extended solution) is provided. Extraction results preprocessed by Data Loader can be reviewed side-by-side with the original document. Users can directly verify hierarchy, categories, reading order, and more, and post-correct necessary parts, enabling them to manage document structure and semantic relationships to fit actual business purposes. <\/p>\n\n Hancom Data Loader<\/a> is a document parsing solution that converts HWP, HWPX, PDF, and OOXML into structured data<\/strong>. The extracted data can be integrated with Hancom\u2019s RAG solution, Hancompedia, enabling an end-to-end workflow\u2014from document collection to search and answers\u2014built on a single Hancom stack. Hancom Data Loader provides a document preprocessing environment that agents can trust. <\/p>\n\n \ud83d\udc49Go to Hancom Data Loader Live Demo<\/strong><\/a><\/p>\n\n \ud83d\udc49<\/strong>Inquire about Hancom Data Loader Implementation<\/strong><\/a><\/p>\n\nWhy Document Parsing Quality Matters in a RAG Pipeline<\/strong><\/strong><\/h3>\n\n
<\/figure>\n\nDocument parsing solution: definition and core role of Hancom Data Loader<\/strong><\/strong><\/h2>\n\n
Hancom Data Loader: an automated document parsing solution<\/strong><\/h3>\n\n
From Document Structure Analysis to Data Extraction<\/strong><\/h3>\n\n
<\/figure>\n\nDocument Data Extraction Targets: Support for Multiple Document Formats<\/strong><\/h3>\n\n
Core Format Support for Public-Sector AI Adoption: HWP\/HWPX Parsing<\/strong><\/strong><\/h4>\n\n
Universal Support for All Document Formats \u2013 PDF and OOXML<\/strong><\/h4>\n\n
Unstructured Visual Documents: PNG\/JPG<\/strong><\/h4>\n\n
Key Features of Hancom Data Loader: DLA\/TSR\/OCR-Based Document Structure Analysis<\/strong><\/strong><\/strong><\/h2>\n\n
DLA \u2013 AI-Based Structure Analysis That Extracts Metadata<\/strong><\/strong><\/strong><\/h3>\n\n
Table\/Image\/Chart Structure Recognition: TSR Technology at the Core of Unstructured Data Processing<\/strong><\/strong><\/strong><\/h3>\n\n
Data Separation and Post-Editing: Data Loader Studio<\/strong><\/h3>\n\n
<\/figure>\n\nHancom Data Loader for document-based AI<\/strong><\/h2>\n\n
\ud83d\udda5\ufe0f<\/strong>Hancom Data Loader<\/strong><\/a><\/h3>\n\n
\n\n