Enterprise-Grade Document Intelligence Platform: Unstructured Data Governance Solution Based on Large Language Models

This article introduces an open-source enterprise-grade document intelligence processing platform developed by shreejoysarkar (GitHub link: https://github.com/shreejoysarkar/Enterprise-Grade-Document-Intelligence-platform-using-Large-Language-Models-LLMs-, released on May 29, 2026, under the MIT open-source license). The platform uses large language model technology to convert internal unstructured documents (such as PDFs, Word files) into queryable structured knowledge bases. Its core architecture consists of three layers: document parsing layer, intelligent chunking layer, and vector indexing layer, and it can be applied in scenarios like enterprise knowledge management, compliance auditing, and intelligent Q&A. The following floors will detail the background, architecture, technical highlights, and application value of this solution.

In digital transformation, enterprises face three major challenges with massive accumulated unstructured documents (contracts, reports, technical manuals, etc.):

1. Format Diversity Issue: Various formats like PDFs, Word files, scanned documents; traditional OCR text extraction easily loses key information such as table structures and image descriptions.
2. Semantic Understanding Limitations: Keyword search cannot handle complex queries (e.g., "last third quarter's compliance risk reports involving the Asia-Pacific region").
3. Knowledge Silo Effect: Documents are scattered across business systems, lacking a unified knowledge graph and semantic connections, leading to low retrieval efficiency and redundant work.

The platform adopts a three-layer tightly integrated architecture:

1. Document Parsing and Conversion Layer

Uses IBM's Docling library to convert multi-format documents into structured Markdown while preserving original structural information. Supports GPU-accelerated parallel processing, enabling table structure recognition and image description generation (OCR is disabled for native digital documents to improve speed). If GPU initialization fails, it automatically falls back to CPU mode.

2. Intelligent Chunking and Semantic Splitting Layer

Implements a hybrid chunking strategy using the Chonkie library:

• Narrative text: SentenceChunker splits by natural sentence boundaries (~512 tokens per chunk, 50-token overlap);
• Table data: TableChunker splits by rows (max 3 rows per chunk) to maintain structural integrity.

3. Embedding and Vector Indexing Layer

Converts chunked content into vector representations and builds indexes. It presumably integrates RAG components (e.g., OpenAI text-embedding-3/BGE embedding models, FAISS/Chroma/Pinecone vector databases, reordering mechanism), activating documents into a real-time queryable knowledge base.

The platform's technical highlights include:

1. Modularity and Scalability: Core functions are encapsulated in three independent Python modules, allowing flexible replacement of components (e.g., chunking libraries, vector databases);
2. Hybrid Chunking Strategy: Handles both text and tables simultaneously, avoiding structural damage or semantic incoherence;
3. GPU Acceleration and Fallback Mechanism: Uses GPU to improve parsing efficiency, with CPU fallback ensuring hardware compatibility;
4. Comprehensive Logging and Error Handling: Each step has success/failure statistics, facilitating enterprise-level deployment and operation monitoring.

The platform can be applied in multiple scenarios:

• Compliance and Auditing: Quickly retrieve contract clauses, automatically identify risk points, and improve audit efficiency;
• Knowledge Management: Build a unified knowledge base to help new employees quickly access information;
• Intelligent Customer Service: Build a Q&A system based on product manuals to provide 7x24 intelligent support;
• R&D Efficiency: Convert technical documents and API manuals into queryable knowledge bases to accelerate the development process.

The project has the following areas for improvement:

1. Brief README Document: Lacks detailed usage examples and architecture diagrams, which is not user-friendly for new users;
2. Dependency Management: The requirements.txt content is simple and needs to supplement more version constraint information (though versions are locked with uv.lock);
3. Lack of Interactive Interface: Currently a backend processing script, needs to provide a Web interface or API service to facilitate enterprise integration.

This open-source project demonstrates how to combine LLMs with enterprise document processing to build a practical intelligent system. It provides an open-source alternative to expensive commercial software and can serve as an architecture template for internal knowledge bases. In the future, with the development of multimodal models and Agent technology, document intelligence systems will evolve towards the direction of 'able to analyze, reason, and act', becoming an important infrastructure for enterprise digital transformation.