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 form fields with exact page and coordinate references and returns results in both hierarchical JSON and Markdown formats.
Amazon Textract
Amazon Textract is a fully managed machine learning (ML) 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 | AWS 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 | Hierarchical JSON and Markdown with visual grounding (coordinates & page refs). | JSON structured as blocks with bounding boxes, confidence scores, and relationships. |
| Visual grounding | Yes, every element tied to precise document positions and coordinates. | Yes, with bounding boxes and spatial metadata, but often requires additional logic for semantic reconstruction. |
| Handwriting detection | Supports diverse layouts including handwriting via visual parsing. | 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 & ecosystem | API-first with SDKs, Snowflake native app, tailored for data pipelines. | AWS service with deep integration to S3, Lambda, IAM, CloudWatch, and broader AWS tools. |
| Pricing model | Typically credits or subscription; enterprise terms vary. | Pay-per-page via AWS; free tier available and pricing varies by feature/API. |
| Best fit use cases | Complex table/form extraction, traceability workflows. RAG/LLM pipelines | AWS-centric applications; general OCR, forms, invoices, contracts, identity and expense docs. |
Technical Differences
Extraction Approach
ADE treats documents as visual systems, understanding structural relationships and layout hierarchies. It identifies text blocks, tables, form fields, and other elements with precise coordinates, returning hierarchical JSON or Markdown 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 (words, lines, key-value pairs, tables, cells) with bounding polygons and confidence scores. Developers parse and reconstruct semantic relationships programmatically. Specialized Analyze APIs (AnalyzeDocument, AnalyzeExpense, AnalyzeID) target specific document types.
Output and Downstream Readiness
ADE outputs hierarchical JSON and Markdown with built-in layout and coordinate mapping, directly usable in RAG, search indices, or LLM pipelines without post-processing.
Textract's output JSON 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 SDKs (Python, TypeScript) and Snowflake marketplace integration for data platforms and AI pipelines.
Textract integrates deeply with AWS storage (S3), compute (Lambda), and monitoring services (CloudWatch), enabling comprehensive 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 hierarchical outputs for RAG and LLM applications
- Schema-based extraction across large document sets (customer onboarding, contracts, healthcare records)
Amazon Textract
- Automating extraction from widely used document types: invoices, receipts, contracts, forms, and identity documents
- Building scalable AWS service 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 hierarchical, visually grounded outputs reduce 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 parse with type validation.
Performance: Textract scales with AWS infrastructure for high throughput; ADE's processing designed for high-fidelity extraction in complex layouts with documented rate limits.
Frequently Asked Questions
What file formats do these services support?
ADE supports 30+ formats including PDFs, images (JPEG, PNG, TIFF, BMP, HEIC), Word documents (DOC, DOCX), PowerPoint presentations (PPT, PPTX), Excel spreadsheets (XLSX), and CSV files in a unified processing workflow. All formats are processed through the same Parse API with consistent output structure. Textract supports PDF, PNG, JPEG, and TIFF formats for document analysis. Both platforms handle multi-page documents and scanned images, though ADE's broader format support eliminates pre-conversion steps for Office documents.
Can ADE outputs be used directly in LLM workflows?
Yes. ADE's structured hierarchical output and visual grounding are designed for direct ingestion into RAG and LLM pipelines without post-processing. Semantic chunking creates a document graph where each chunk knows its type (text, table, image, form_field) and relationships to other chunks, enabling LLMs to understand document structure.
Can I try ADE before committing?
Yes. Use the ADE Playground to test your actual documents and see parsed outputs without writing code. The Playground supports drag-and-drop file upload, shows JSON and Markdown outputs 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 under 5 minutes using the Python SDK or TypeScript SDK. Code examples demonstrate common workflows including schema-based extraction, document splitting, and RAG pipeline integration.