Product Overviews
LandingAI ADE
LandingAI Agentic Document Extraction (ADE) is a document intelligence platform designed to convert complex documents into reliable structured data. It identifies elements such as text, tables, and figures with exact page and coordinate references, and returns results as structured Markdown alongside a hierarchical structure.
Amazon Textract
Amazon Textract is a fully managed machine learning document analysis service from AWS. It automatically extracts printed and handwritten text, layout elements, forms, tables, and structured data from scanned documents and images. Textract returns its findings as JSON with bounding box coordinates, confidence scores, and relationship mappings.
Core Capability Comparison
| Capability | LandingAI ADE | Amazon Textract |
|---|---|---|
| Primary approach | Layout-aware visual parsing with hierarchical outputs designed for LLM and RAG ingestion | Machine learning OCR and document analysis that extracts text, forms, tables, and layout elements |
| Output formats | Structured Markdown and a hierarchical structure, with a page reference and bounding box on every element | JSON structured as blocks with bounding boxes, confidence scores, and relationships |
| Visual grounding | Yes, every element tied to a precise document position | Yes, with bounding boxes and spatial metadata, though often requiring additional logic for semantic reconstruction |
| Handwriting | Processes handwritten content alongside printed text | Handles printed and handwritten text in many document types |
| Schema-based extraction | Schema definitions for targeted field extraction after parsing | Custom queries feature allows tailored extraction via pretrained query models |
| Integration and ecosystem | API-first with Python and TypeScript libraries; Snowflake Marketplace app; built for data pipelines | AWS service with deep integration to S3, Lambda, IAM, CloudWatch, and broader AWS tools |
| Pricing model | Credit-based, or subscription; enterprise terms vary. See Plans & Billing | Pay-per-page via AWS; free tier available and pricing varies by feature and API |
| Best fit use cases | Complex table and form extraction, traceability workflows, RAG and LLM pipelines | AWS-centric applications; general OCR, forms, invoices, contracts, identity and expense documents |
Technical Differences
Extraction Approach
ADE treats documents as visual systems, understanding structural relationships and layout hierarchies. It identifies text, tables, figures, and other elements with precise coordinates, returning structured Markdown and a hierarchical structure suited for AI workflows. Schema-based extraction handles repetitive field extraction across document batches with minimal configuration.
Textract employs ML models to detect text and analyze structure, returning lists of blocks such as words, lines, key-value pairs, tables, and cells, with bounding polygons and confidence scores. Developers parse and reconstruct semantic relationships programmatically. Specialized Analyze APIs target specific document types.
Output and Downstream Readiness
ADE outputs structured Markdown and a hierarchical structure with built-in layout and coordinate mapping, directly usable in RAG, search indices, or LLM pipelines without post-processing.
Textract output contains rich metadata but typically requires additional parsing and organization logic for complex documents or AI applications.
Ecosystem and Integration
ADE provides API-first design with Python and TypeScript libraries, plus a Snowflake Marketplace app for data platforms and AI pipelines. The Snowflake app runs on ADE v1.
Textract integrates deeply with AWS storage, compute, and monitoring services, enabling automated document workflows inside AWS environments.
Use Cases
LandingAI ADE
- Extracting structured data from complex multi-column documents, forms, financial statements, and mixed media PDFs
- Preparing visually grounded structured outputs for RAG and LLM applications
- Schema-based extraction across large document sets such as customer onboarding, contracts, and healthcare records
Amazon Textract
- Automating extraction from widely used document types: invoices, receipts, contracts, forms, and identity documents
- Building scalable AWS workflows integrated with object storage, serverless compute, and analytics
- Key-value and table extraction for structured data pipelines in enterprise systems
Practical Considerations
Output complexity. ADE's structured, visually grounded output reduces post-processing needs. Textract's block-based JSON may require custom transformation logic to assemble structured records.
Cloud footprint. ADE works across platforms with API integration and EU region support. Textract optimizes for AWS environments with integrated billing and service ecosystem.
Customization. Textract's custom query feature allows extraction tailored to business document types via pretrained query setups. ADE uses schema definitions applied after parsing, with type validation.
Performance. Textract scales with AWS infrastructure for high throughput. ADE is designed for high-fidelity extraction on complex layouts, with documented rate limits.
Frequently Asked Questions
What file formats do these services support?
ADE v2 accepts PDFs and images, including JPEG, PNG, and, through the API, TIFF, BMP, GIF, and WEBP. Office documents and spreadsheets are supported on ADE v1, as are password-protected PDFs.
Textract supports PDF, PNG, JPEG, and TIFF for document analysis. Both platforms handle multi-page documents and scanned images.
Can ADE outputs be used directly in LLM workflows?
Yes. ADE's structured output and visual grounding are designed for direct ingestion into RAG and LLM pipelines without post-processing.
The parsed result is a hierarchical structure where each element carries its type, its position in reading order, and its grounding to the source, so LLMs can reason about document structure rather than a flat string of text.
Can I try ADE before committing?
Yes. Use the ADE Playground to test your own documents and see parsed output without writing code. It supports drag-and-drop upload, shows JSON and Markdown side by side, and displays visual grounding with bounding boxes overlaid on source pages.
For API integration testing, follow the quickstart guide to get an API key and run your first parse in minutes using the Python or TypeScript library.