LandingAI Agentic Document Extraction (ADE)
How ADE Works
Provides three APIs for document processing: Parse, Split, and Extract.
- Parse: Converts documents into hierarchical JSON and Markdown with page numbers and coordinates. Required first step for all workflows.
- Split: Separates multi-document files into individual sub-documents based on type.
- Extract: Pulls specific fields using your defined schema rules with coordinate-level grounding.
Key Features
- 99.16% accuracy on DocVQA benchmark
- Detects text, tables, images, form fields, barcodes, and signatures
- Visual grounding with coordinates for audit trails
- Layout-agnostic parsing without templates
- Returns Markdown and hierarchical JSON
- Multi-language support
- Supports PDFs, images, Word, PowerPoint, spreadsheets
Use Cases
- Finance: KYC processing, loan applications, financial statements, compliance reporting
- Healthcare: Clinical forms, insurance claims, patient records
- Legal: Contracts, court filings, regulatory submissions with audit trails
Nanonets
How Nanonets Works
Uses AI models to extract content from documents with flexible output formats and workflow automation capabilities.
- Real-time streaming via SSE for interactive feedback
- Batch processing up to 50 documents per request
- Custom instructions guide extraction focus and formatting
- Synchronous and asynchronous processing modes
Key Features
- Multiple output formats (Markdown, HTML, JSON, CSV)
- Bounding boxes at block and word level
- Confidence scoring for extracted fields
- Custom instruction support for tailored extraction
- Multilingual extraction (29+ languages)
- Workflow automation with instant learning
- Pre-trained models for common document types
Use Cases
- Invoice and receipt processing with field-level extraction
- Form automation and data entry workflows
- Document conversion for analytics and reporting
Tensorlake
How Tensorlake Works
Document ingestion API combined with serverless Python workflows for end-to-end document processing pipelines.
- Layout-aware parsing to Markdown or JSON
- Serverless workflow runtime with durable execution
- VLM-powered classification and summarization
- Integrated orchestration for multi-step document workflows
Key Features
- 91.7% F1 on enterprise document benchmarks, 86.79% TEDS on OmniDocBench table parsing
- Layout detection with reading order preservation
- Table recognition with complex cell handling (1,500+ cells)
- Signature and barcode detection with bounding boxes
- Figure and table summarization for LLM consumption
- Strikethrough detection (99% accuracy)
- Durable workflows with checkpointing and fault recovery
- GPU/CPU auto-scaling for processing pipelines
- VPC and on-premise deployment options
Use Cases
- Compliance: Contract analysis, signature verification, document classification
- Data Pipelines: Multi-step ETL workflows with LLM integration and vector search preparation
Core Capabilities Compared
| Capability | LandingAI ADE | Nanonets | Tensorlake |
|---|---|---|---|
| Document Understanding | Vision-first parsing with semantic chunking | AI extraction with custom instructions | VLM-powered layout understanding |
| Layout Preservation | Strong visual parsing; hierarchical relationships | Standard table/form detection | Layout-aware with reading order |
| Structured Output | Schema-controlled JSON with coordinates | Flexible: JSON, CSV, Markdown, HTML | Structured JSON with bounding boxes |
| Auditability | Page numbers and coordinates per chunk | Bounding boxes and confidence scores | Bounding boxes with citations |
| Accuracy | 99.16% on DocVQA | Standard OCR accuracy | 91.7% F1 on enterprise docs |
| Unique Features | Zero Data Retention, HIPAA BAA, VPC deployment, Snowflake integration | Real-time streaming, instant learning workflows | Serverless workflows, durable execution, strikethrough detection |
Why ADE
- Parse Once, Query Unlimited: ADE’s Document Pre-trained Transformer (DPT-2) parses documents into structured outputs that preserve complete document information.
- Schema-Controlled Extraction with Grounding: Every extracted field links to exact page locations and bounding boxes, creating audit trails required in regulated industries.
- Production-Ready SDKs and Integration: Python and TypeScript SDKs provide typed interfaces for Parse, Split, and Extract operations. Async processing for large files handles enterprise document volumes, while comprehensive error handling and rate limit documentation support production deployment.
When to Choose Which Tool
LandingAI ADE: Best for enterprises requiring schema-controlled extraction with compliance features (audit trails, HIPAA, SOC 2). Handles variable document layouts without templates.
Nanonets: Suitable for teams needing flexible output formats and workflow automation. Real-time streaming and batch processing capabilities work well for high-volume document conversion and business process automation.
Tensorlake: Optimal when document extraction feeds multi-step orchestration workflows requiring durable execution. Combines document ingestion with serverless Python workflows for complex data pipelines.
Frequently Asked Questions
What accuracy does LandingAI ADE achieve?
ADE achieved 99.16% accuracy on the DocVQA validation split (5,286 correct out of 5,331 questions answered using only parsed output, no image access). Production accuracy depends on document quality.
How does Tensorlake’s accuracy compare?
Tensorlake achieved 91.7% F1 score on enterprise document structured extraction and 86.79% TEDS on OmniDocBench table parsing, outperforming Azure (88.1% F1, 78.14% TEDS) and AWS Textract (88.4% F1, 80.75% TEDS) on their benchmark.
What is visual grounding and why does it matter?
Visual grounding links every extracted field to its exact page location and bounding box coordinates in the source document. This creates audit trails required for regulatory compliance, enables human verification of extracted values, and supports citation-based retrieval systems where users need to see source evidence.