API-first Agentic Document Intelligence platform built for accuracy, reliability, and governance at scale.
Mountain View, CA
Joined December 2017
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LandingAI retweeted
From the stage at AI Dev 26:
Dan Maloney, CEO of @LandingAI, on the Future of Software Engineering: AI is compressing skill gaps. Whether you’re a generalist or a specialist, you can fill gaps faster and move across domains more easily. The bar for building is shifting, and that's a good thing.
Stay tuned for more AI Dev highlights!
LandingAI retweeted
At AI Dev 26, @LandingAI will showcase Agentic Document Extraction (ADE), an API-first platform that turns messy, multi-modal documents and dark data into structured, auditable intelligence.
Meet their team at booth 107 on April 28 and 29!
Try your document at hubs.la/Q04cZQqR0
LandingAI retweeted
Systems for information retrieval can sometimes fail at the very first step: document processing. Especially when teams are rehosting their workflows on cloud environments.
Join us for "Document AI: From OCR to Agentic Doc Extraction" built in partnership with @LandingAI. You’ll learn to implement RAG with agents to parse and extract information in the important (but often overlooked!) document processing step.
Then you’ll explore AWS services like S3, Lambda and Bedrock to rebuild your system as a production-ready pipeline.
Start today! hubs.la/Q0416sw_0
LandingAI retweeted
OCR can process characters but it doesn’t understand pixels. OCR has no way to reason about the headers, totals, or checkboxes found in tables, invoices, or forms.
In our course with @LandingAI, "Document AI: From OCR to Agentic Doc Extraction," we build agents to address these failure modes by breaking documents into pieces, applying the right tools, and mapping information to expected formats.
Learn more and enroll today: hubs.la/Q0407Tzs0
LandingAI retweeted
Traditional OCR is great... until it hits real documents.
Scanned invoices with merged cells. Medical records with handwritten notes. Contracts with multi-column layouts.
Everything breaks.
The text gets extracted. But the structure disappears. Tables flatten. Reading order scrambles. Relationships between elements vanish.
This is the gap between pulling text and understanding documents.
We built a short course with @DeepLearningAI that walks through closing that gap.
You'll see how document processing evolved from traditional OCR to Agentic Doc Extraction:
→ Why OCR fails on complex layouts
→ How layout detection preserves structure
→ What reading order does for multi-column documents
→ How vision language models capture text and images together
→ How ADE parses documents without losing context
6 hands-on labs. Each one builds on the previous.
By the final lab, you're deploying an event-driven pipeline on AWS that automatically processes documents uploaded to S3 and enables semantic search with visual grounding.
The course is free.
Enroll here: bit.ly/4bpV2rG
LandingAI retweeted
OCR (optical character recognition) can read text from documents, but it often misses what matters most: layout and visual structure, like tables, charts, forms, and reading order.
In "Document AI: From OCR to Agentic Doc Extraction," a new short course built with @LandingAI, you’ll learn how to go beyond OCR using Agentic Document Extraction (ADE): parsing documents as visual objects and extracting structured Markdown and JSON, grounded to specific regions on the page.
Taught by David Park and Andrea Kropp.
Start now: bit.ly/3Lmuqxf
LandingAI retweeted
New course: Document AI: From OCR to Agentic Doc Extraction, built with @LandingAI, where I'm executive chairman, and taught by David Park and Andrea Kropp.
Much of the world's data is locked in PDFs, JPEGs, and other documents. This short course shows you how to build agentic workflows that process documents accurately: breaking them into parts, examining each piece carefully, and extracting information through multiple iterations.
Traditional Optical Character Recognition (OCR) captures text but loses context from table headers, chart captions, or reading order of columns. After exploring OCR's limitations, you’ll use LandingAI's Agentic Document Extraction (ADE) framework to process documents. ADE treats pages as visually -- as images -- to parse information and extract fields.
Skills you'll gain:
- Build agents to convert unstructured files into structured Markdown/HTML and JSON
- Use ADE to parse complex data like forms, handwriting, or equations
- Map extracted information to named fields using a specified schema, with bounding boxes for grounding and validation
- Deploy RAG applications with event-driven document processing
Come learn about the best tools for processing documents like financial invoices, medical records, or academic papers intelligently:
deeplearning.ai/short-course…
LandingAI retweeted
This week, in The Batch, Andrew Ng introduces Landing AI's latest Agentic Document Extraction (ADE) tool, which accurately converts PDFs into LLM-ready markdown text for use in industries like healthcare, finance, and law.
Plus:
🌐 OpenAI’s Stargate expands with new U.S. and U.K. sites
🧬 AI generates viral genomes
🎵 Sweden pilots opt-in music license for AI training and compensation
🗺 AlphaEarth Foundations releases global Earth embeddings for accurate mapping
Read The Batch: hubs.la/Q03Mb2Wp0
LandingAI retweeted
ABB + @LandingAI = Faster, smarter robot vision.
LandingLens™ cuts time to train AI vision systems by 80% with no-code tools.
new.abb.com/news/detail/1290…
LandingAI retweeted
Agentic Document Extraction now supports field extraction! Many doc extraction use cases extract specific fields from forms and other structured documents. You can now input a picture or PDF of an invoice, request the vendor name, item list, and prices, and get back the extracted fields. Or input a medical form and specify a schema to extract patient name, patient ID, insurance number, etc.
One cool feature: If you don't feel like writing a schema (json specification of what fields to extract) yourself, upload one sample document and write a natural language prompt saying what you want, and we automatically generate a schema for you.
See the video for details!
LandingAI retweeted
Agentic Document Extraction just got much faster! From previous 135sec median processing time down to 8sec. Extracts not just text but diagrams, charts, and form fields from PDFs to give LLM-ready output. Please see the video for details and some application ideas.
LandingAI retweeted
🚀 Try the Agentic Document Extraction Playground:
va.landing.ai
🔗 SDK for batch processing and advanced features:
github.com/landing-ai/agenti…
LandingAI retweeted
🎉 Exciting news! We teamed up with @landingAI to show how VisionAgent landing.ai/visionagent boosted its authentication with Logto.
Check out the full case study to see it in action. Packed with tech insights and useful tips! 👀👉 logto.io/stories/landing-ai
#CaseStudy #Logto #Authentication #TechCollab
LandingAI retweeted
Agentic AI systems are becoming the go to method for using AI to solve complex tasks. We recently released a new version of Vision Agent, an agentic library aimed at solving vision tasks. Here's an example of Vision Agent determining how full a jar of coffee beans is:
LandingAI retweeted
We’re building an open source Vision Agent to bridge LMMs and practical computer vision applications. If you’re interested in either using a Vision Agent or joining our community to work on this with us, come join us on discord! discord.gg/wPdN8RCYew
LandingAI retweeted
What does the AI landscape look like this week? From new marketplace rules for video games to car companies integrating generative AI into their products, dive into the week's top news, summarized for you on Data Points, a spin-off of The Batch.
Read now: hubs.la/Q02gVPcf0
LandingAI retweeted
How can #LVMs help enterprises address the challenges resulting from the recent increase in unstructured data?
Join us and @SnowflakeDB on Thursday for a deep dive with industry experts!
Register here: bit.ly/41xqM7c
@YangK_ai #Manufacturing #ComputerVision