easybits Extractor vs. Unsiloed

The easybits Extractor functions as a reliable community node for n8n, facilitating the extraction of structured data from various unstructured documents. Users can simply point it at document types such as PDFs, images, scans, invoices, receipts, or contracts, indicate the necessary fields in simple language at extractor.easybits.tech, and receive well-organized, easily branchable JSON data in return. To cater to different needs, it employs two separate engines: the General engine (Gemini), which provides broad understanding capabilities, and the Specialized OCR-optimized engine (Mistral), specifically engineered to manage dense scans with high efficiency. The free plan enables users to extract up to 10 fields per workflow and allows for 50 API requests monthly, making it a practical option for those looking to create and implement effective workflows without financial investment. This feature-rich offering is especially beneficial for individuals seeking to optimize their data extraction tasks seamlessly and efficiently.

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.