Compare SIMQIN vs. Unsiloed

In contemporary settings, XML stands as a prevalent standard for multimedia and cross-platform technical document publishing. A multitude of editing applications, management systems, and publication tools, along with various industry standards, rely heavily on XML. Despite its widespread adoption, XML has struggled to gain traction in certain domains due to its distinct separation between document structure and layout. Editors often lean towards using word processors like Microsoft Word, where text and formatting are developed concurrently, allowing for immediate visual feedback. Conversely, XML demands a more abstract mindset, as the content is interspersed with structural tags, with the layout being established only in later production phases. In contrast, SimQin enhances the user experience by presenting documents in layout mode from the beginning while also revealing the underlying XML structure and organization. This dual visibility can streamline the editing process and potentially increase XML's appeal among users accustomed to traditional word processing tools.

Unsiloed AI is an enterprise document intelligence platform built to transform unstructured documents into structured, LLM-ready data. The platform processes PDFs, images, spreadsheets, scans, and multimodal files, then outputs clean JSON, Markdown, or structured fields for AI agents, LLM applications, vector databases, and data warehouses. Its core capabilities include parsing, extraction, and document splitting, allowing teams to use each function independently or chain them into a full production pipeline. Unsiloed’s parser converts complex documents into Markdown while preserving structure across text, tables, charts, figures, forms, handwriting, signatures, and visual hierarchy. Its extraction engine pulls schema-specific fields into JSON and uses domain awareness to understand documents such as invoices, contracts, financial reports, healthcare records, and regulatory filings. Its splitting tools can separate mixed files into individual documents or break long documents into retrievable chunks while preserving parent-child relationships and surrounding context. The platform is powered by proprietary dual-stream vision models that combine a data stream for tokens and entities with a layout stream for bounding boxes, alignment, indentation, and visual structure. Unsiloed is designed to solve the problem of fragile OCR and DIY pipelines that break when document layouts change. For enterprise AI teams, Unsiloed provides a more reliable document layer for turning high-value unstructured data into assets that can be searched, reasoned over, and used in production AI systems.