Unsiloed vs. Box Extract

Unsiloed AI is a document layer for enterprise AI that converts complex unstructured files into clean JSON, Markdown, and structured data. The platform is built for organizations whose most valuable information lives inside PDFs, scanned documents, images, spreadsheets, contracts, invoices, reports, filings, forms, and other hard-to-parse formats. Unsiloed helps AI teams avoid building brittle OCR, parser, and post-processing pipelines by providing a production-ready API for document parsing, field extraction, and document splitting. Its parsing capability converts PDFs, scans, and images into LLM-ready Markdown while preserving tables, figures, text hierarchy, page structure, signatures, handwriting, and visual context. Its extraction capability pulls specific fields into JSON using schemas, confidence thresholds, and domain-aware logic that can understand context such as line items, payment terms, clauses, totals, and references. Its splitting capability separates multi-document files into individual documents and breaks long files into retrievable chunks for RAG, agent workflows, and search systems. Unsiloed uses proprietary dual-stream vision models that process content and layout in parallel, then fuse them through cross-attention so the system can reason over what a document says and how it is structured. The platform’s architecture includes attention-guided heatmaps, typed document regions, layout-aware processing, and domain-specific decoding for industries such as finance, legal, healthcare, and enterprise operations. It is designed to handle edge cases that traditional OCR often misses, including nested tables, merged cells, multi-page tables, figures, handwritten notes, forms, and documents with mixed formats. Teams can connect data sources such as S3, SharePoint, Drive, Snowflake, or a document management system, then send structured outputs into LLMs, AI agents, vector databases, or analytics warehouses.

Box Extract is a cutting-edge tool that leverages artificial intelligence to efficiently identify, collect, and convert structured data from unstructured sources such as documents, PDFs, spreadsheets, images, and other formats into organized metadata that facilitates easy storage, searching, and utilization, ultimately improving business operations. The technology employs sophisticated large language models, optical character recognition (OCR), chain-of-thought prompting, and specialized retrieval-augmented generation combined with reasoning techniques to achieve a profound comprehension of document content and structure with remarkable accuracy, all while eliminating the necessity for extensive training or complex setups. Users can choose between Standard and Enhanced Extract Agents, capable of handling everything from basic fields like names and dates to complex components such as hazardous clauses, tables, and graphs. Moreover, they have the ability to develop Custom Extract Agents utilizing configurable metadata templates, which allows for efficient management across numerous folders and repositories. This adaptability empowers organizations to customize the tool according to their unique requirements, thereby enhancing both efficiency and effectiveness in data management. As a result, businesses can experience a significant reduction in time spent on data extraction tasks, leading to more streamlined workflows and improved overall productivity.