Document intelligence transforms complex documents into structured, machine-readable data at scale.
LandingAI Agentic Document Extraction (ADE) interprets visual layouts, preserves document structure, handles complex tables including merged cells, and extracts structured fields with exact coordinate grounding. The output is structured data ready for direct use in databases, analytics platforms, and AI applications.
ADE Document Intelligence Workflow
Every workflow starts with Parse, followed by optional Extract based on your requirements.
Parse (required). Converts documents into structured Markdown and a hierarchical tree of typed elements. Segments documents into text, tables, figures, signatures and stamps, and machine-readable codes such as barcodes and QR codes. Accepts PDFs and images on ADE v2; Office documents and spreadsheets are supported on ADE v1. See the Parse API and how to parse invoices with ADE.
Split (optional). Classifies and separates parsed documents into multiple sub-documents based on document types you define. Use it when one file contains multiple documents that need separation, such as batched KYC documents. Split runs on ADE v1 and is in Preview.
Extract. Pulls specific data fields from parsed documents using a JSON schema you define, such as invoice total or patient name. Every extracted value links back to the location in the source document it was read from. See the Extract API.
Document Challenges in Financial Services
Traditional OCR and basic text extraction fail on real-world financial documents for several reasons.
Complex table structures with merged cells. Bank statements and loan applications contain multi-level tables where cells span multiple columns or rows. Traditional OCR flattens these relationships, losing the structure that connects account balances to transaction histories.
Multi-page statements with inconsistent layouts. Monthly statements and credit reports span dozens of pages with varying formats. Generic parsers struggle to maintain reading order when layouts change mid-document, so critical data is extracted out of sequence or missed.
Scanned and low-quality images. Many financial documents arrive as faxed forms, photocopied applications, or mobile-uploaded images with skew, shadows, or compression artifacts. Basic OCR degrades rapidly on poor-quality scans.
Handwritten notes, checkmarks, and signatures. Loan applications include handwritten income figures, KYC forms contain manual signatures, and compliance documents have checkbox attestations. Standard OCR ignores or misinterprets these elements.
Mixed content types on single pages. A mortgage application combines structured form fields, tables showing asset details, signature blocks, and embedded statements. Generic parsers treat each element independently, breaking relationships between data points.
Nested tables and financial formulas. SEC filings and credit memos embed tables within tables, with calculations requiring exact numeric precision. Flattening destroys parent-child relationships between summary rows and detail lines.
Key ADE Use Cases in Financial Services
KYC and Client Due Diligence
Banks receiving Know Your Customer packages typically get batched PDFs containing multiple document types per customer: passports, utility bills, bank statements. Each has a different layout, and the required set varies by jurisdiction and customer profile.
A global Tier-1 bank deployed ADE for its Client Due Diligence operations across multi-lingual corporate documents averaging 200 to 300 pages per client, achieving a 40% to 60% reduction in manual review time and saving hundreds of analyst hours per week. The Tier-1 bank KYC case study covers deployment scope, document types, and compliance requirements in detail.
ADE extracts structured fields from each document type without configuration. The Split API separates mixed document types from a single batched PDF into classified sub-documents for downstream processing.
Mortgage and Loan Origination
A complex loan packet combines W-2s, 1040s with schedules, pay stubs, bank statements, title reports, and appraisal documents. These come from thousands of different employers, preparers, and institutions, each with their own layout.
Autyn, an AI-native mortgage operations platform, uses ADE as the extraction layer for its income engine. It reports 94% to 98% field-level accuracy on borrower documents that broke its previous OCR-plus-LLM pipeline, with end-to-end income extraction cut from the one to two hours it takes a human down to one to three minutes, across 500 or more loan files and 3,000 or more documents in production.
Account Reconciliation and Transaction Matching
ADE converts PDF and scanned statements into structured Markdown and a hierarchical tree, with each transaction row preserved as an individually grounded table cell. Row and column relationships stay intact for downstream reconciliation systems.
Regulatory Compliance and Audit
ADE grounds every extracted field to a specific page and region in the source document, producing the field-level provenance trail that CFPB mortgage examinations, BSA/AML reviews, and SR 11-7 model risk assessments require.
Claims and Dispute Resolution
ADE handles the visual heterogeneity of supporting documents without templates or training, so claims workflows process documents from any insurer, jurisdiction, or format variant without per-source configuration.
Security, Compliance, and Deployment Considerations
Zero Data Retention (ZDR): with the ZDR option enabled, customer data is not persisted beyond processing. Your data is used exclusively to perform the operation you request, and processing ends when the output is returned to you. LandingAI does not use your data for training or improving its models when ZDR is active. A separate setting extends ZDR to the Playground.
Compliance certifications: SOC 2 Type II certified. Supports HIPAA workflows through a Business Associate Agreement, which requires ZDR enabled. GDPR compliant, with EU regional deployment available.
Data residency:
- US: AWS US (Ohio)
- EU: AWS EU (Ireland)
Deployment options:
- SaaS (cloud-hosted): fully managed service with automatic scaling.
- VPC deployment: containerized app in your own Virtual Private Cloud on AWS, Azure, or GCP. ADE maintains zero data retention because it is on your VPC, and you control infrastructure and retention.
- On-premises: deployed within your infrastructure for institutions with the strictest security requirements.
Encryption: all data in transit is encrypted using TLS 1.2 or higher, and data at rest with AES-256. In VPC and on-premises deployments, you control the infrastructure and retention.
Frequently Asked Questions
How does ADE handle complex financial tables with merged cells?
ADE uses vision-first parsing that interprets table structure visually rather than relying on gridlines or text patterns. Merged cells, nested tables, and multi-level headers are preserved.
Every cell is returned individually with its own grounding and its position in the grid, so row and column relationships stay intact and any value traces back to its exact cell.
What happens to sensitive financial data after processing?
With Zero Data Retention enabled, customer data is not persisted beyond processing, across the entire platform and all subprocessors. Without ZDR, documents are retained according to the terms of your agreement, as outlined in the Trust Center.
What file formats does ADE support?
ADE v2 accepts PDFs and images, including JPEG, PNG, TIFF, BMP, GIF, and WEBP. Word documents, PowerPoint presentations, spreadsheets, and password-protected PDFs are supported on ADE v1.
How does ADE pricing work for financial institutions?
ADE uses credit-based pricing. The Explore plan is pay-as-you-go with free credits to start, Team adds Zero Data Retention and BAA coverage, and Enterprise offers custom pricing, volume discounts, and dedicated support SLAs. VPC and on-premises deployments have separate licensing models.
For current rates, see Credit Consumption. For plan details, see Plans & Billing.