LandingAI Agentic Document Extraction (ADE)
How ADE Works
LandingAI ADE provides two core v2 APIs for document processing: Parse and Extract. Splitting and classification are ADE Gen 1 operations, in preview.
- Parse: converts documents into structured Markdown and a typed block tree, each block grounded with a page number, a Markdown character range, and a normalized bounding box. Required first step for all workflows.
- Extract: pulls specific fields using a JSON schema, each field grounded to a character range in the source Markdown.
- Split and Classify (ADE Gen 1): the Split API separates multi-document files into sub-documents, and the Classify API labels documents by type.
Key Features
- 99.16% accuracy on the DocVQA dataset
- Types content into blocks: text, tables, figures, logos, ID cards, signatures and stamps (
attestation), and barcodes or QR codes (scan_code) - Visual grounding: page number, Markdown range, and normalized bounding box per block, for audit trails
- Layout-agnostic parsing without templates
- Returns Markdown and a typed block tree (
structure) - Multi-language support
- Parses PDFs and images; Office documents and spreadsheets are supported on ADE Gen 1
Use Cases
- Finance: KYC processing, loan applications, financial statements, compliance reporting. See document intelligence in financial services.
- Healthcare: clinical forms, insurance claims, patient records. See document processing for healthcare.
- 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 (Tensorlake's published figures)
- 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 into a typed block tree | AI extraction with custom instructions | VLM-powered layout understanding |
| Layout Preservation | Strong visual parsing; hierarchical page and block relationships | Standard table/form detection | Layout-aware with reading order |
| Structured Output | Structured Markdown plus a typed block tree; schema-controlled fields via Extract | Flexible: JSON, CSV, Markdown, HTML | Structured JSON with bounding boxes |
| Auditability | Page, Markdown range, and bounding box per block | Bounding boxes and confidence scores | Bounding boxes with citations |
| Accuracy | 99.16% on DocVQA | Standard OCR accuracy | 91.7% F1 on enterprise docs (Tensorlake's benchmark) |
| Unique Features | Zero Data Retention, HIPAA BAA, VPC deployment, Snowflake app (ADE Gen 1) | 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) models parse documents into structured outputs that preserve complete document information, so you parse once and run many extractions against that output without reprocessing.
Schema-Controlled Extraction with Grounding: every extracted field links back to its source, a character range in the parsed Markdown, traceable to the page and bounding box of the block it came from, creating the audit trails required in regulated industries. See how ADE makes every extraction defensible.
Production-Ready SDKs and Integration: Python and TypeScript SDKs provide typed interfaces for the Parse and Extract operations. Async processing (Parse Jobs and Extract Jobs) handles enterprise document volumes, while documented error handling and rate limits 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 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?
On Tensorlake's published benchmark, it reports 91.7% F1 on enterprise document structured extraction and 86.79% TEDS on OmniDocBench table parsing, ahead of Azure (88.1% F1, 78.14% TEDS) and AWS Textract (88.4% F1, 80.75% TEDS). These figures are Tensorlake's own.
What is visual grounding and why does it matter?
Visual grounding links every extracted value to its page, its character range in the source Markdown, and a bounding box. This creates the audit trails required for regulatory compliance, enables human verification of extracted values, and supports citation-based retrieval where users need to see source evidence.