LandingAI
LandingAI's Agentic Document Extraction (ADE) is a vision-first API that converts complex PDFs, forms, and scanned documents into structured, citation-grounded data — scoring highest among agentic document extraction tools with 99.16% accuracy on DocVQA.
1B+
Documents Processed
99.16%
DocVQA Accuracy
50+
Enterprise Customers
50+
Languages
Overview
LandingAI's Agentic Document Extraction (ADE) is a commercial API that converts complex, unstructured documents into structured, machine-readable data using proprietary vision-first transformer models (DPT-2) rather than generic OCR + LLM stacks. Founded in 2017 by Andrew Ng (co-founder of Coursera, founding lead of Google Brain), LandingAI raised $57M and serves regulated industries including financial services, insurance, healthcare, and legal. ADE's key differentiator is citation grounding: every extracted chunk returns the page number, bounding box coordinates, and confidence score — making it the strongest choice for compliance-sensitive workflows where you need to prove where every data point came from. It scores highest among agentic document extraction tools in independent benchmarks (69/100, beating Mistral OCR, Claude Sonnet, and OpenAI o3-mini).
The Verdict
Who Should Use LandingAI ADE?
Best For
- Regulated industries needing auditable extraction — every output includes page number, bounding box, and confidence score
- Complex financial documents with dense tables, merged cells, and mixed text+table on the same page
- Healthcare and legal workflows requiring HIPAA compliance and zero data retention guarantees — practitioners in medical document processing report standardizing on ADE as their sole extraction vendor
- Large document batches (1,000+ pages) where smart chunking and agentic verification matter
- Teams processing forms with signatures, checkboxes, barcodes, or handwriting
Not Ideal For
- Cost-sensitive projects at scale — credit-based pricing is harder to forecast than flat-rate alternatives
- Teams wanting a self-hosted or open-source option — ADE is cloud API only
- Rapid RAG prototyping where LlamaParse's native LlamaIndex integration is faster to wire up
- Workflows needing webhooks or a built-in human-in-the-loop review UI — not currently available
What's Great
- Highest benchmark score among agentic document extraction tools — 69/100 (aimultiple), beating Mistral OCR, Claude Sonnet 3.7, OpenAI o3-mini
- 99.16% accuracy on DocVQA benchmark
- Best-in-class auditability — every extraction grounded with page number, bounding box coordinates, and confidence score
- Handles complex tables with merged cells, nested structures, and mixed text+table layouts without manual prompting
- Handwriting recognition and checkbox/signature/barcode detection built in
- SOC 2 Type II, HIPAA, GDPR compliant with zero data retention option
- Composer AI agent auto-experiments with prompts and schemas to maximize extraction accuracy
- Smart chunking handles 1,000+ page files without size limits
Watch Out For
- Credit-based pricing makes cost forecasting difficult at scale — no flat per-page rate. After the 1,000 free credits run out (~330 pages), parsing runs ~$30 per 1,000 pages at standard DPT-2 rates, climbing toward $40 once extraction is added — users report it's the most expensive option in the category
- No webhook support — limits real-time integration patterns
- No built-in human-in-the-loop review UI for validating extractions
- No workflow orchestration or evaluation framework — pipeline assembly is on the developer
- Cloud API only — no self-hosted or on-premise option
- LandingAI as a company also makes LandingLens (computer vision for manufacturing) — can create confusion about what ADE actually is
Pricing
Explore
Free
1,000 free credits (~330 pages at 3 credits/page on DPT-2). Single seat. For development and validation. ($1 = 100 credits after free tier.)
Team
$250/mo
27,500 credits/month ($1 = 110 credits — 10% bonus). Unlimited seats, email support, HIPAA BAA, zero data retention.
Enterprise
Custom
VPC/on-prem option, dedicated SLA, Snowflake native app, custom pipelines. Contact sales.
View all features & details
Three Core APIs
- Parse API — transforms documents into layout-aware markdown with precise citations (page, bounding box, confidence)
- Split API — segments multi-document files and classifies mixed document types within a single PDF
- Extract API — pulls specific fields using user-defined JSON schemas (flat, nested, arrays, multi-table)
Key Capabilities
- Proprietary DPT-2 (Document Pre-trained Transformer) models — not generic OCR + LLM
- Coordinate grounding on every extraction (page, bounding box, confidence score)
- Complex table handling: merged cells, nested structures, mixed text+table layouts
- Handwriting recognition
- Signature, checkbox, and barcode detection
- Composer AI — auto-experiments with prompts/schemas to maximize accuracy
- Smart chunking for 1,000+ page files
- 50+ language support — users report strong results on non-English documents including Hebrew, though accuracy trails English and depends on scan/input quality
Compliance & Security
- SOC 2 Type II certified
- HIPAA compliant (BAA available on Team+)
- GDPR compliant
- Zero data retention option
- VPC / on-premise deployment (Enterprise)
Use Cases
- Financial: loan underwriting, KYC, regulatory reporting
- Insurance: claims processing and document verification
- Healthcare: medical records, clinical support, prior authorizations
- Legal: due diligence, contract extraction
- Logistics: shipping documents, invoices, bills of lading
- RAG pipelines: citation-grounded retrieval for enterprise AI apps
Integrations & SDKs
- Python SDK
- TypeScript SDK
- REST API
- Snowflake Native App
- No-code playground for testing schemas before production
Company Background
- Founded 2017 by Andrew Ng (Google Brain, Coursera)
- $57M Series A (2021) — McRock Capital, Intel Capital, Samsung Catalyst
- 1B+ images and documents processed
- Processing time: under 2 seconds per document
How It Compares
| Feature | LandingAI ADE | LlamaParse | Unstructured | Docling |
|---|---|---|---|---|
| Benchmark Score | 69/100 (aimultiple #1) | Good | Good | Good |
| Citation Grounding | Page + bbox + confidence | None | Limited | None |
| Complex Tables | Best-in-class | Inconsistent | 75% accuracy | 97.9% accuracy |
| Handwriting / Forms | Yes | Limited | Partial | No |
| Self-hosted | No | No | Yes | Yes |
| Free Tier | 1,000 credits | 10K credits/mo | 15K pages | Free (MIT) |
| HIPAA + SOC 2 | Yes (Team+) | No | Yes | No |
| Pricing | ~$0.03/page | $0.0013–$0.056/page | $0.03/page | Free |
| Best For | Regulated industries + audits | RAG speed | Compliance + breadth | Accuracy + privacy |
User Reviews
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