Unsiloed vs. Quantxt Theia

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.

Gathering information from both digitized and physical documents is vital for contemporary enterprises. No matter the intricacy or design of these documents, they can be transformed into a well-organized and machine-readable format. This automation in document processing facilitates effective management of various business documentation types. By converting both scanned and electronic materials into structured data, companies can leverage this refined information for numerous downstream applications, such as database storage or spreadsheet exports. This advanced solution goes beyond the limitations of basic OCR and conventional document parsing, as merely extracting unformatted text often falls short for many use cases. It is essential to convert text and data contained within documents, of any magnitude, into structured information. This method not only improves the scale and efficiency of business operations but also automates the data extraction process, leading to immediate enhancements in workflow. By enabling the processing of a significantly greater volume of documents, businesses can diminish the reliance on extra staff for document management and lower the chances of human error. Ultimately, this transformative capability not only streamlines operations but also significantly boosts productivity across the entire organization, allowing teams to focus on more strategic tasks. Enhanced efficiency in document handling paves the way for better decision-making and innovation within the company.