Unsiloed AI

What is Unsiloed AI?

Unsiloed AI is a specialized document processing platform designed to transform complex, unstructured documents—particularly financial reports, annual filings, earnings statements, and investment documents—into clean, structured data. Leveraging proprietary vision-language models combined with advanced OCR and segmentation techniques, the platform extracts accurate information from PDFs, PowerPoint presentations, Word documents, and images with unprecedented precision. Unlike traditional OCR solutions that fail with varying layouts or generic LLMs that struggle with deterministic extraction, Unsiloed AI employs a dual-stream architecture that preserves both content and structural hierarchy, ensuring reliable data extraction for accuracy-sensitive applications. The platform is built for regulated environments and integrates seamlessly into RAG pipelines, knowledge bases, and AI automation workflows.

Key Features

• Proprietary Vision-Language Models

  Domain-specific VLMs purpose-built for financial data extraction, combining vision understanding with OCR capabilities to handle complex layouts and preserve document structure.

• Multimodal Document Processing

  Process PDFs, PowerPoint, Word documents, images, tables, charts, and webpages with intelligent segmentation and semantic chunking for accurate content extraction.

• Structured Data Output

  Automatically transform unstructured content into JSON or Markdown formats with confidence scoring, enabling reliable integration with downstream AI systems.

• Semantic Chunking

  Advanced document chunking strategies including semantic grouping, hierarchical relationships, and paragraph-level organization for better context preservation in AI applications.

• Financial Domain Specialization

  Optimized for processing regulatory filings, earnings reports, and investment documents with domain-specific decoders ensuring regulatory compliance and accuracy.

• Open Source Components

  Publicly available Python library (Unsiloed Parser) for document preprocessing and chunking, enabling developers to build custom RAG pipelines and AI workflows.

Use Cases

• Financial Data Extraction : Automatically parse and extract structured data from financial documents, regulatory filings, and earnings statements for analysis and reporting.
• RAG Pipeline Development : Build robust Retrieval-Augmented Generation systems with preprocessed, accurately chunked documents for improved context retrieval and AI comprehension.
• Enterprise Document Processing : Automate bulk processing of complex business documents across regulated environments with high accuracy and compliance requirements.
• Knowledge Base Creation : Transform unstructured document repositories into organized, queryable knowledge bases for AI chatbots and automated research systems.
• Fintech and Investment Analysis : Enable investment firms and fintech companies to extract insights from thousands of financial documents quickly and accurately.

FAQs