Agentic Document Extraction

Agentic Document Extraction (ADE) is a developer-focused API designed to extract structured contents from complex and unstructured documents—including PDFs, images, scanned forms, multi-column layouts, tables, and embedded visuals.

ADE provides accurate data extraction from diverse document layouts without the need for model training or fine-tuning. Simply submit a document to receive structured output in JSON or Markdown format. The Markdown format is optimized for integration into workflows, enabling easy routing of content into platforms like Snowflake for vectorization or directly into vector databases for downstream applications such as retrieval-augmented generation (RAG) and search.

Documents can be loaded directly from Snowflake internal or external stages, with the resulting structured data written back into your environment. The solution is designed to support production use cases across various industries, including financial reporting, loan analysis, medical forms, compliance documentation, and other document-intensive workflows.

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**Key features:**

**Accurate Extraction**<br/>ADE accurately processes a variety of formats—including handwritten and printed text, multi-column layouts, structured and semi-structured forms, complex tables with merged cells, checkboxes, annotations, and embedded images. ADE is designed to handle both noisy, scanned documents and clean digital PDFs with equal proficiency, providing high-accuracy field-level data extraction without requiring custom rules or model fine-tuning.<br/><br/>**Contextual Understanding and Chunking**<br/>Proprietary vision model groups and arranges information semantically, mimicking human reading patterns to enhance the quality of extraction through the integration of visual context within documents.<br/><br/>**Visual Grounding**<br/>ADE API outputs bounding box coordinates and page numbers for all extracted information to facilitate downstream traceability, validation, and compliance.

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**Field Extraction**

Field Extraction enables you to directly extract structured data from documents. For example, if you have a huge set of invoices you can extract the date, vendor name, and items listed in every invoice. This reduces repetitive document processing to extract specific fields from large collections of documents without writing custom parsing functions. This provides easier programmatic access to documents, making it easy to evaluate and compare extracted data across multiple documents.

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原始信息汇总

Agentic Document Extraction 数据集概述

数据集提供商

LandingAI

数据集描述

Agentic Document Extraction (ADE) 是一个面向开发者的API，旨在从复杂和非结构化的文档中提取结构化内容，包括PDF、图像、扫描表格、多栏布局、表格和嵌入式视觉内容。ADE无需模型训练或微调即可从多样化的文档布局中准确提取数据，输出格式为JSON或Markdown。

主要功能

• 准确提取：处理手写和打印文本、多栏布局、结构化和半结构化表格、复杂表格（含合并单元格）、复选框、注释和嵌入式图像。
• 上下文理解和分块：专有视觉模型模拟人类阅读模式，通过视觉上下文增强提取质量。
• 视觉基础：输出所有提取信息的边界框坐标和页码，便于下游可追溯性、验证和合规性。

适用行业

• 财务报告
• 贷款分析
• 医疗表格
• 合规文档
• 其他文档密集型工作流

集成支持

• 与Cortex Search和Cortex Agent无缝集成。
• 支持从Snowflake内部或外部阶段直接加载文档，并将结构化数据写回用户环境。

安全特性

• 符合Native Apps Framework安全要求。
• 基于角色的访问控制（RBAC）。

定价

需联系提供商获取定价信息。

云区域可用性

AWS

• 亚太地区（孟买）
• 亚太地区（大阪）
• 亚太地区（首尔）
• 亚太地区（新加坡）
• 其他25个区域

数据刷新

静态数据

联系方式

• 销售：[e-mail]
• 技术支持：[e-mail]

相关数据集

• LandingLens - Visual AI Platform
• Sample Dataset for LandingLens: Manufacturing Metal Casting
• Sample Dataset for LandingLens: LifeSciences Pneumonia

类别

• AI & ML
• 金融
• 健康和生命科学

法律条款

标准条款