IDP Accelerator: Agentic Document Intelligence from Extraction to Compliance Validation
Md Mofijul Islam, Md Sirajus Salekin, Joe King, Priyashree Roy, Vamsi Thilak Gudi, Spencer Romo, Akhil Nooney, David Kaleko, Boyi Xie, Bob Strahan, Diego A. Socolinsky

TL;DR
IDP Accelerator introduces an agentic AI framework for comprehensive document understanding, extraction, and compliance validation, utilizing multimodal LLMs, secure data access, and rule-based validation, demonstrated with high accuracy and efficiency in healthcare.
Contribution
The paper presents a novel end-to-end framework with four key modules, including a new dataset and multimodal classifier, enhancing document processing capabilities beyond traditional pipelines.
Findings
Achieved 98% classification accuracy in healthcare documents
Reduced processing latency by 80%
Lowered operational costs by 77% compared to legacy systems
Abstract
Understanding and extracting structured insights from unstructured documents remains a foundational challenge in industrial NLP. While Large Language Models (LLMs) enable zero-shot extraction, traditional pipelines often fail to handle multi-document packets, complex reasoning, and strict compliance requirements. We present IDP (Intelligent Document Processing) Accelerator, a framework enabling agentic AI for end-to-end document intelligence with four key components: (1) DocSplit, a novel benchmark dataset and multimodal classifier using BIO tagging to segment complex document packets; (2) configurable Extraction Module leveraging multimodal LLMs to transform unstructured content into structured data; (3) Agentic Analytics Module, compliant with the Model Context Protocol (MCP) providing data access through secure, sandboxed code execution; and (4) Rule Validation Module replacing…
Peer Reviews
No public reviews on file for this paper yet. If you reviewed it on a platform where reviews are public (OpenReview, ICLR, NeurIPS, ICML), you can paste yours below so the community can read it here.
Videos
No videos yet. Explain this paper in a talk, walkthrough, or lecture? Add one.
Taxonomy
TopicsTopic Modeling · Text and Document Classification Technologies · Biomedical Text Mining and Ontologies
