LandingAI

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.

Key features: Accurate Extraction 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. Contextual Understanding and Chunking 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.

Visual Grounding ADE API outputs bounding box coordinates and page numbers for all extracted information to facilitate downstream traceability, validation, and compliance. 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.