Landing AI
Landing AI is now promoting its enterprise document processing platform. The official stated that ADE can convert any document into auditable structured data. The results come with page number coordinate references and confidence levels, and support cloud or private deployment.
Updated: 2026-10-26
What is Landing AI?
The official website of Landing AI currently focuses on the document intelligence product line Agentic Document Extraction (ADE), positioning itself as "Accurate, production-ready AI for real-world documents": converting any document into auditable, production-ready structured data. The difference between it and the common "OCR plus LLM" solution is clearly stated in the FAQ on the official website—the results come with citations of page numbers and precise coordinates, and each block has a confidence score, so it can first be processed by machines and then the low-confidence parts can be handed over to human review. The official website states that it has served more than 50 corporate customers, processed a total of over 1 billion images and documents, and the single-page processing time is within 2 seconds; it has now been iterated to ADE Gen2, and the in-site announcement shows that the V2 API saves 25% to 80% of the quota compared with the old version.
Who is it for?
- Financial and insurance institutions for material extraction in credit approval, KYC and regulatory reporting
- Healthcare, legal, energy, and logistics industries that process contracts and archives with variable formats
- Data teams preparing semantically segmented structured corpus for RAG and knowledge bases
- Operations and compliance teams looking to reduce manual review and automate document workflows
- For enterprises with strict requirements that data must not leave the network, opt for on-premises or private deployment
Key features
6Parse (Parsing)
Converts scanned documents with variable layouts, dense tables, forms, and multi-format documents into hierarchy-preserving, LLM-friendly Markdown. It outputs content blocks such as text, tables, and images, and provides page number and coordinate references for each block.
Split (Splitting)
Automatically splits long files containing multiple mixed documents into well-classified sub-documents, supports large files with hundreds of pages, and can perform instance splitting based on recurring identifiers such as invoice numbers, dates, and order numbers.
Extract
Extract fields according to a custom schema, supporting flat or nested structures, arrays, and multiple tables. The official website states that it can handle large tables spanning multiple pages with thousands of rows, and each value comes with bounding box references for auditing purposes.
Verifiable Accuracy
The official website emphasizes "Accuracy you can prove, not guess". The results can be verified against the original text using page numbers and coordinate references, and confidence scores will proactively highlight sections that may require manual review.
Performance and Scale
The official website states that ADE can process thousands of pages per minute, with a single-page processing time of under 2 seconds; the V2 API reduces credit consumption by 25% to 80% compared to the older version.
Security & Deployment
The official website lists SOC 2 Type II certification, GDPR and HIPAA compliance, three deployment methods (cloud, on-premises, and virtual private), and an option for zero data retention; integrations include REST API as well as Python and TypeScript libraries.
How to use Landing AI
- First, use the free official portal to run a test with your own real files.
- Focus on the analysis results of complex tables and multi-page documents, and open the reference coordinates to check if they can be traced back to the original position — this is the most valuable point for comparison with pure text extraction schemes.
- For field extraction, it is recommended to first define a minimal usable schema (field types, whether nested, and whether arrays are required).
- After getting it to work, add fields gradually.
- this is easier to converge on than writing a complex schema all at once.
- For new projects, directly use the V2 API, which has lower credit consumption.
- In the production environment, be sure to retain the safety net of "confidence threshold plus manual review" and do not let errors silently flow downstream.
- For compliance and medical scenarios, prioritize confirming the deployment method and zero data retention options, and confirm with the sales team before signing the contract.
Traffic & popularity
Data month Sep 2026
Landing AI receives about 21.3K visits per month and ranks #1,315,604 worldwide.
Monthly visits
12 monthsTraffic sources
- Search39.9%
- Direct29.7%
- Referrals12.0%
- Social6.9%
- Mail6.1%
- Other5.5%
Top countries
- United States25.7%
- India16.8%
- Nigeria6.5%
- Japan5.4%
- Germany4.5%
Estimated from third-party traffic data.
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Nutrient Data Extraction APINutrient's cloud document extraction interface parses PDF, scanned documents, images and Office files into spatial JSON or Markdown with coordinates, confidence and page number context. It can also extract by field name with reference sources. The official website states that the free quota is 5,000 credits per month.