Unsiloed vs. ApPost

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.

ApPost is a software application aimed at extracting and interpreting information from digital documents, with a key emphasis on handwritten material. It proficiently processes both structured and unstructured documents by accurately reading numeric and alphabetic data, along with handwritten text that may not have been part of its initial training; it is capable of dynamically updating and quickly refreshing its vocabulary as required. Conversely, N.I.Te focuses on advanced software technologies for the automatic handling of documents, especially handwritten ones, whether derived from still images or real-time data captured by various devices. The innovative solutions from N.I.Te can interpret handwritten text even in the absence of a predefined vocabulary, thereby overcoming challenges that many other solutions in the market encounter. Furthermore, a significant advantage of their technology lies in its ability to learn effectively from a small number of training examples, which enhances adaptability and overall performance. This adaptability and innovation place both ApPost and N.I.Te at the forefront of the rapidly advancing field of document processing software, where their respective capabilities are continuously pushing the boundaries of what's possible. As the demand for automated document processing grows, the importance of such technologies will only increase, further solidifying their positions as industry leaders.