IDP Accelerator: Agentic Document Intelligence from Extraction to Compliance Validation
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arXiv
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| Autores principales: | , , , , , , , , , , |
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| Formato: | Preprint |
| Publicado: |
2026
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| Materias: | |
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| _version_ | 1866915890524585984 |
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| author | Islam, Md Mofijul Salekin, Md Sirajus King, Joe Roy, Priyashree Gudi, Vamsi Thilak Romo, Spencer Nooney, Akhil Kaleko, David Xie, Boyi Strahan, Bob Socolinsky, Diego A. |
| author_facet | Islam, Md Mofijul Salekin, Md Sirajus King, Joe Roy, Priyashree Gudi, Vamsi Thilak Romo, Spencer Nooney, Akhil Kaleko, David Xie, Boyi Strahan, Bob Socolinsky, Diego A. |
| contents | 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. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2602_23481 |
| institution | arXiv |
| publishDate | 2026 |
| record_format | arxiv |
| spellingShingle | IDP Accelerator: Agentic Document Intelligence from Extraction to Compliance Validation Islam, Md Mofijul Salekin, Md Sirajus King, Joe Roy, Priyashree Gudi, Vamsi Thilak Romo, Spencer Nooney, Akhil Kaleko, David Xie, Boyi Strahan, Bob Socolinsky, Diego A. Computation and Language 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. |
| title | IDP Accelerator: Agentic Document Intelligence from Extraction to Compliance Validation |
| topic | Computation and Language |
| url | https://arxiv.org/abs/2602.23481 |