LandingAI’s DPT-2 in 2026: Why Agentic Document Extraction Finally Makes Sense

Documents are the dark matter of enterprise data. They’re everywhere - contracts, lab reports, invoices, insurance filings, clinical notes - and they carry the information organizations rely on to make decisions. Yet for decades, extracting structured data from them has been a painful mix of brittle templates, hand-tuned regex patterns, and OCR pipelines that fall apart the moment a table loses its gridlines or a signature sits on top of a paragraph. LLMs made the problem feel solvable. You could paste a PDF’s text into a prompt and ask for structured output.