Unsiloed vs. PageIndex

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.

PageIndex stands out as a sophisticated AI platform focused on understanding complex and lengthy documents, delivering precise and verifiable answers that are closely tied to the original materials. Instead of utilizing techniques like embeddings, chunking, or vector databases, it adopts a reasoning-based retrieval approach that organizes each document into a tree-like structure, mirroring how people typically navigate through sections, subsections, pages, and various content types. A reasoning model meticulously examines this hierarchy to pinpoint relevant information, facilitating context-aware retrieval that remains both transparent and easily explainable. Users can upload a diverse array of documents, including reports, legal contracts, academic papers, technical guides, medical documents, textbooks, and business strategies, enabling them to ask questions that feature line-level citations for comprehensive validation. PageIndex excels in managing extremely lengthy documents that could extend to thousands of pages while also being skilled in interpreting not just text but also tables, charts, figures, and images. This holistic understanding guarantees that users can effectively gather significant information from extensive resources, enhancing their ability to make informed decisions based on a wide range of data. Ultimately, PageIndex serves as a powerful tool for anyone seeking to navigate the complexities of large documents with ease and accuracy.