Layout-aware parsing
Parse documents into structured data with layout-aware understanding. The product describes LLM-ready Markdown and preserved hierarchy for text, tables, and figures.
LandingAI
LandingAI provides agentic document extraction APIs that convert complex documents into structured, auditable data for enterprise workflows. It targets developer teams building automation for compliance, onboarding, loan processing, retrieval, and reporting.
Overview
LandingAI provides Agentic APIs for intelligent document processing, centered on Agentic Document Extraction (ADE). The product is built to convert documents into structured, auditable data for enterprise workflows such as compliance review, onboarding, loan processing, and document-based retrieval.
Its core workflow covers parsing, splitting, and extracting data from complex, high-variance documents including multi-page files, dense tables, forms, and multilingual content. The site emphasizes traceability through page numbers, coordinates, bounding-box citations, and confidence scoring, so teams can verify outputs rather than treat them as opaque results.
Core capabilities
Document splitting and classification
Split large files and mixed-document PDFs into classified sub-documents, including long multi-hundred-page batches and repeated identifiers such as invoice numbers or dates.
Schema-first field extraction
Extract flat or nested schemas, arrays, and large tables across many pages. The product also supports bounding-box citations for each extracted value.
Audit-ready provenance
Ground extracted results back to pages, coordinates, and table cells so teams can review where values came from and route uncertain items to humans.
Production document workflows
Handle multilingual documents and confidence scoring for results that may need review. The home page emphasizes production use on complex layouts rather than template-heavy workflows.
API and developer integration
Expose the system through REST APIs plus Python and TypeScript libraries so engineering teams can embed extraction into downstream automation.
Use cases
Financial services compliance
Process KYC and client due diligence documents where analysts need structured outputs, traceability, and support for large, non-standard corporate files.
Loan and mortgage processing
Extract borrower income and related fields from stacks of tax returns, paystubs, and bank statements to reduce manual loan review time.
Plan and compliance review
Turn plan sets, code-related documents, and review packets into auditable structured data for compliance and issue-tracking workflows.
Document retrieval and analytics
Build retrieval and analytics pipelines that need semantically chunked, grounded content from mixed document archives and long PDFs.
Operations automation
Automate downstream reporting, reconciliation, and approval workflows by sending structured document data into existing internal systems.
Pros and Cons
Pros
- Supports parsing, splitting, and extraction in one document-processing platform.
- Provides auditability with page, coordinate, and table-cell grounding.
- Handles complex layouts, large tables, multi-page files, and multilingual documents.
- Offers API access plus Python and TypeScript libraries for developer workflows.
- Includes deployment and security options for regulated environments, including cloud, VPC, on-premises, and zero data retention options.
Cons
- The product is aimed at regulated and document-heavy workflows, so it is less oriented toward simple one-off file conversion tasks.
- Some details that buyers often want, such as setup steps, supported output formats beyond the cited structured outputs, and broader integration lists, are not fully documented on the pages provided.
- Pricing page details suggest multiple deployment options, but exact enterprise terms still require a quote.
FAQ
What plans does LandingAI offer?
LandingAI’s Agentic Document Extraction is designed to parse, split, and extract structured data from documents through API-based workflows. The pricing page describes an Explore plan for developers validating a use case, a Team plan for teams shipping document workflows, and an Enterprise plan for custom infrastructure needs.
How does credit usage work?
The pricing page says credits are the usage unit, and each document processing task consumes credits based on the number of pages processed. Explore is pay-as-you-go, while Team and Enterprise include monthly allotments.
What does the product do in practice?
The pricing page lists modular capabilities such as parsing, field extraction, visual grounding, document splitting and classification, and multilingual document handling. The home page also describes structured outputs with page-level and coordinate-level citations.
How does it fit into existing systems?
The site says LandingAI supports modular REST APIs and Python or TypeScript libraries. Case studies also show it integrating into existing workflows, including a KYC platform and AWS-based deployments.
What deployment options are available?
The pricing page and home page indicate flexible deployment options, including cloud, on-premises, and virtual private deployment options. The site also mentions zero data retention options, HIPAA/BAA availability on Team, and VPC/on-prem support on Enterprise.
Quick Facts
- Category
- Developer Tool
- Product type
- Agentic document extraction APIs
- Primary users
- Enterprise developers and teams handling document workflows
- Source domain
- landing.ai
- Key workflows
- Parsing, splitting, field extraction, auditability, and document classification
- Deployment options
- Cloud, VPC, on-premises, and virtual private deployment
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