@LandingAI

API-first Agentic Document Intelligence platform built for accuracy, reliability, and governance at scale.

 Mountain View, CA
landing.ai/
Joined December 2017
  • Tweets3K
  • Following854
  • Followers10.1K
978Media

Streaming parsed documents into Snowflake at volume

Extracting fields from one document is a solved problem. Doing it for thousands a day, and landing the results somewhere your team can query, is where most pipelines stall. We built this one to close …

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Extracting structured data from brokerage statements and tax forms

A typical investor holds positions at several brokerages, a retirement account or two, and a stack of tax paperwork each spring. Each account produces its own documents in its own format, and almost n…

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Building an immigration financial audit pipeline on AWS

Reviewing six months of bank statements for a single visa applicant takes two to four hours. A reviewer opens each statement, reconstructs the balance history, checks the total against a funding thres…

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Building Invoice-to-Contract Compliance with Agentic Document Extraction

Every enterprise negotiates a contract, and every enterprise then pays invoices that break it. The overcharge lives in plain sight. A reviewer catches it only if they open the contract, find the right…

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Serverless Document Processing with ADE and AWS Lambda on S3

Production document pipelines fail on operational shape long before they fail on model quality. Documents arrive from many sources at unpredictable intervals, and the extraction layer has to run on ev…

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Building a Multi-agent AI System for Intelligent Loan Processing and Fraud Detection

Manual loan review breaks down on the same expensive, repetitive work: pulling names, income, balances, addresses, and identity details out of a stack of borrower documents, then checking whether thos…

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Automating reinsurance treaty intake with document extraction and human-in-the-loop review

Reinsurance underwriters spend 40% of their time on admin work. Most of it is retyping treaty data into contract systems.

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Automating OTC margin calls with document extraction and multi-agent normalization

Meet the Margin Collateral Agent, a project built during the Financial AI Hackathon Championship 2025 that automates one of the most disputed workflows in OTC derivatives.

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How to Build a Financial Knowledge Graph from PDFs

Financial institutions spend $206B a year on financial crime compliance. The majority of that spend goes toward one activity: manually assembling relationships across flat documents.

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Why Long Documents Break Extraction (And What Actually Works)

Production document extraction systems fail predictably. They fail when files get long, schemas get complex, tables span pages, and field labels shift across suppliers. The problem isn't context windo…

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