IDP Accelerator: Agentic Document Intelligence from Extraction to Compliance Validation

2026-02-26

AI Summary

This work addresses the limitations of traditional document processing systems in handling multi-document inputs, complex reasoning tasks, and stringent compliance requirements, which hinder efficient extraction of structured information. To overcome these challenges, the authors propose an end-to-end intelligent document processing framework that integrates document segmentation, multimodal large model-based information extraction, agent-driven analysis via the MCP protocol, and LLM-powered rule validation to achieve full pipeline automation. Key contributions include the creation of DocSplit, a multimodal document classification benchmark; the design of an agent-based analytical architecture that supersedes conventional rule engines; and support for executing sophisticated compliance logic within a secure sandbox. Deployed in a healthcare setting, the system achieves 98% classification accuracy, reduces processing latency by 80%, and cuts operational costs by 77%. The implementation is open-sourced with an online demo available.

Technology Category

• Natural Language Processing: Information Extraction
• Machine Learning: Large Multimodal Models (LMMs)
• Cognitive Modeling & Cognitive Systems: Agent Architectures

Application Category

• Semantics and Knowledge: Data modeling to support human-machine intelligence, including LLMs agents, intelligent system behavior, explanations, and user-friendly interactions
• Systems and Infrastructure for Web, Mobile and WoT: Applied ML and AI for Web-based mobile applications
• Economics, Online Markets and Human Computation: Cost models of using LLMs in production 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 deterministic engines with LLM-driven logic for complex compliance checks. The interactive demonstration enables users to upload document packets, visualize classification results, and explore extracted data through an intuitive web interface. We demonstrate effectiveness across industries, highlighting a production deployment at a leading healthcare provider achieving 98% classification accuracy, 80% reduced processing latency, and 77% lower operational costs over legacy baselines. IDP Accelerator is open-sourced with a live demonstration available to the community.

Problem

• document intelligence
• unstructured documents
• compliance validation
• multi-document processing
• structured data extraction

Innovation

• Agentic AI
• Multimodal LLM
• Document Intelligence
• Compliance Validation
• Model Context Protocol