Upstage Document Parse vs. Unsiloed

Upstage Document Parse is a powerful tool designed to transform complex documents—such as PDFs, scanned images, spreadsheets, and presentations—into structured HTML or Markdown that machines can readily interpret, all while ensuring high-speed and accuracy suitable for enterprise needs. With advanced layout understanding, it skillfully recognizes intricate tables, charts, and coordinates, processing each page in roughly 0.6 seconds, which allows for the processing of 100 pages in under a minute—5 to 10 times faster than its competitors—while achieving over 5% higher accuracy in layout and table detection, boasting TEDS scores of 93.48 and TEDS-S scores of 94.16. The tool can be easily integrated through a REST API, is available for on-premises deployment, or can be utilized via cloud platforms like AWS, facilitating a smooth incorporation into existing workflows with user-friendly client libraries. Its versatile applications range from enhancing enterprise search functionalities and delivering AI-powered document summaries to digitizing legal and compliance documentation and optimizing financial report processing, all while maintaining precise layouts and ensuring that outputs are clean and searchable for future applications. Additionally, this innovative technology aids organizations in refining their data management practices and boosting their overall operational efficiency, ultimately driving productivity and ease of access across various sectors.

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.