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LandingAI has overhauled its document intelligence stack with DPT-3 Pro and Verity, shifting from flat chunking to a hierarchical tree structure that provides atomic grounding to specific lines or words. This release introduces character-based billing alongside deterministic transcription for digital documents, offering enterprises flexible deployment options ranging from public clouds to air-gapped on-premises environments. The strategic split between high-volume text handling and complex layout analysis aims to optimize cost efficiency while improving the precision of extracted data.

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LandingAI Releases Agentic Document Extraction Gen2 with DPT-3 Pro and DPT-3 Verity

MarkTechPost10 septembrie 20265 min de cititArticolul și perspectiva sunt în engleză.

LandingAI has shipped Agentic Document Extraction Gen2, a rebuild of its document stack on the DPT-3 model family. Chunks are retired in favor of a document, page and block tree. DPT-3 Pro grounds to the line, DPT-3 Verity grounds to the word with a confidence score, and Parse billing now counts output characters instead of flat pages. Gen1 code will not run against Gen2 endpoints. The post LandingAI Releases Agentic Document Extraction Gen2 with DPT-3 Pro and DPT-3 Verity appeared first on MarkTechPost.

LandingAI has shipped Agentic Document Extraction (ADE) Gen2, a rebuild of its document intelligence stack around a new model family called DPT-3. Gen1 treated a document as a flat list of chunks. Gen2 treats it as a tree, prices it by the characters it returns rather than by the page, and grounds every answer back to a specific line or word on the page. LandingAI team frames the release around three themes: affordability, agent ready outputs, and atomic grounding. Is it deployable? Yes. ADE…

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