According to Andrew Ng (@AndrewYNg), LandingAI has launched a new course titled 'Document AI: From OCR to Agentic Doc Extraction,' taught by David Park and Andrea Kropp (source: Andrew Ng on Twitter, Jan 14, 2026). The course addresses the widespread challenge of extracting structured data from unstructured documents such as PDFs and JPEGs. It covers practical techniques for building agentic document extraction systems using advanced optical character recognition (OCR) and AI-driven automation. This initiative offers concrete business opportunities for enterprises dealing with large volumes of document-based data, helping them automate workflows, improve data accuracy, and enable faster decision-making through AI-powered document processing (source: Andrew Ng on Twitter, Jan 14, 2026).

According to @DeepLearningAI, the new course 'Document AI: From OCR to Agentic Doc Extraction' developed with LandingAI introduces Agentic Document Extraction (ADE), which surpasses traditional OCR by enabling AI models to interpret documents as visual objects. This approach allows extraction of structured data, including tables, charts, and reading order, outputting formats like Markdown and JSON mapped to specific regions on the page. The course, taught by David Park and Andrea Kropp, demonstrates practical applications of ADE for business automation, document analytics, and workflow integration, offering significant efficiency improvements over legacy OCR solutions (source: @DeepLearningAI, Jan 14, 2026).

According to Andrew Ng, Agentic Document Extraction, powered by the document pre-trained transformer (DPT) model, successfully extracted key financial metrics such as NVIDIA's $57.01B quarterly revenue from the latest 10-Q earnings report released just an hour ago (source: Andrew Ng on Twitter). The AI-driven extraction tool demonstrated high accuracy by comparing the original PDF directly to the structured output, showcasing immediate and reliable document data extraction capabilities for financial analysis and compliance. This advancement in document AI highlights business opportunities for automated financial reporting, due diligence, and enterprise workflow automation, enabling faster and more precise insights for AI-powered enterprise solutions (source: Andrew Ng on Twitter).

According to DeepLearning.AI, Andrew Ng introduced Landing AI's new Agentic Document Extraction (ADE) tool, designed to accurately convert PDF documents into markdown text optimized for large language models (LLMs). This innovation targets high-demand sectors like healthcare, finance, and law, where efficient and precise data extraction enables streamlined document management and improved automation workflows (source: DeepLearning.AI on Twitter, Oct 3, 2025). The Batch also reported on OpenAI's Stargate expansion in the U.S. and U.K., the use of AI to generate viral genomes, Sweden's pilot of opt-in music licensing for AI training, and AlphaEarth Foundations' new global Earth embeddings for enhanced geospatial analysis. These developments highlight significant business opportunities in leveraging AI for document processing, genomic research, IP management, and geospatial intelligence.

According to Andrew Ng, Agentic Document Extraction now supports field extraction, enabling users to extract specific fields such as vendor name, item list, and prices from images or PDFs of invoices and structured documents (source: Andrew Ng, Twitter, July 10, 2025). This upgrade allows businesses to automate data capture from common business documents, significantly reducing manual entry and boosting operational efficiency. The feature is expected to streamline AI-powered document processing workflows, opening new opportunities for enterprises in finance, logistics, and administration to leverage AI for automated data extraction and reduce costs.

According to Andrew Ng, Agentic Document Extraction has dramatically reduced its median PDF processing time from 135 seconds to just 8 seconds. This AI-driven tool now extracts not only text but also diagrams, charts, and form fields from PDFs, producing outputs optimized for large language models (LLMs). This breakthrough enables faster and more comprehensive data extraction, improving automation and accuracy in industries such as finance, legal, and healthcare where rapid document analysis is critical. The speed and versatility of Agentic Document Extraction present significant business opportunities for enterprises seeking to streamline workflows and leverage AI for document-intensive operations (source: Andrew Ng on Twitter, May 27, 2025).