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Agentic Document Extraction
A new suite of agentic vision APIs — document extraction, object detection, and more.

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LandingLens
An end-to-end, low-code platform to label, train, and deploy custom vision models.

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Agentic Document Extraction
A new suite of agentic vision APIs — document extraction, object detection, and more.

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LandingLens
An end-to-end, low-code platform to label, train, and deploy custom vision models.

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Start for Free Choose a platform to continue

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Agentic Document Extraction
A new suite of agentic vision APIs — document extraction, object detection, and more.

Right image

arrow icon

LandingLens
An end-to-end, low-code platform to label, train, and deploy custom vision models.

Right image

Invoices are one of the most common yet difficult documents to automate at scale. Variations in layout, line-item tables, currencies, and scan quality often break traditional OCR and template-based systems. In this post, we explore how Agentic Document Extraction (ADE) parses real-world invoices, grounds every extracted field to its source, and produces reliable, schema-aligned data that holds up across diverse formats and high-volume workflows.
Real-time Radiology AI
Healthcare teams process thousands of radiology reports every day, each packed with important diagnostic information in the form of images and text. When you can analyze these documents in real-time, it opens up possibilities for much faster information access and better clinical workflows.
Enterprises depend on millions of unstructured documents — financial statements, medical forms, contracts, engineering drawings — that traditional OCR and LLM pipelines fail to handle. Without visual context or traceability, automation breaks, accuracy drops, and trust erodes.
Agentic Document Extraction (ADE) by LandingAI automates document-heavy processes in retail banking, including loans, KYC, and compliance. Its layout-aware visual AI extracts structured data accurately across varied formats, reducing manual effort and streamlining operations.
We ran on the DocVQA validation split and got 5,286 correct out of 5,331 (99.16%). Of those 45 wrong answers, only 18 are true parsing shortcomings. DocVQA is usually used to evaluate vision-language models, but we are pioneering the use of this popular dataset to establish the accuracy of our Agentic Document Extraction (ADE) Parse API.
Ava Xia
Signatures, stamps, and seals are the final proof of authenticity in documents, but they are also among the hardest elements to detect automatically. With the new attestation chunk in Agentic Document Extraction (ADE), these visual markers are identified, structured, and extracted with spatial context intact. From healthcare reports and financial forms to legal affidavits, ADE ensures every attestation is captured accurately and ready for reliable, audit-ready automation.
In the era of large language models, a silent barrier has emerged for non-English speakers. Despite the revolutionary capabilities of modern AI, there’s an uncomfortable truth: most mainstream models are trained predominantly on English corpora, creating an implicit performance gap that affects text parsing, information extraction, and document processing across languages. For organizations processing Chinese […]
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Nearly every enterprise runs on documents. Legal agreements, claims packets, forms, loan applications, contracts, just to name a few. These documents can be long, messy, complex, and multimodal, including scanned forms, nested/complex tables, charts, graphs, engineering & architecture diagrams, checkboxes, radio buttons, images, signatures, footnotes and much more. Often, business users spend hours re-keying or […]
Your team processes hundreds of property insurance claims daily. Some arrive as PDFs, others as scanned images. One claim might list the policyholder's name as "John Smith," while another uses "Smith, John." The data you need is in there somewhere, but getting it out consistently? That's the challenge.

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