Ragie

RagieSince 2025-08

Ragie is a fully managed Retrieval-Augmented Generation (RAG) backend engineered to eliminate the operational complexity of building and maintaining custom vector pipelines. It handles document parsing, semantic chunking, embedding generation, vector indexing, and hybrid re-ranking through a single high-performance API endpoint. Instead of configuring separate chunking scripts, vector databases, and re-ranking algorithms, engineering teams connect Ragie directly to their data sources. Ragie continuously keeps embeddings synchronized and provides sub-100ms context retrieval designed specifically for conversational AI assistants and knowledge search engines.

Under the hood, Ragie integrates state-of-the-art document layout models capable of extracting tables, code snippets, PDFs, Notion pages, and Google Docs without formatting degradation. Its retrieval engine blends dense semantic vector search with sparse BM25 keyword matching and cross-encoder re-ranking for maximum recall. Ragie features built-in partition-level access control, ensuring that multi-tenant SaaS applications can isolate tenant data securely while querying a shared knowledge infrastructure.

UnstructuredSince 2023-01

Unstructured is the leading enterprise ETL (Extract, Transform, Load) platform engineered to prepare messy, unstructured business documents for Retrieval-Augmented Generation (RAG) and LLM fine-tuning. Over 80% of enterprise data lives in complex formats like scanned PDFs, PowerPoint decks, Word files, HTML tables, and email threads that break standard text scrapers. Unstructured utilizes specialized computer vision and vision-language models to segment documents into structural semantic elements (titles, paragraphs, headers, embedded tables, and image captions) while preserving exact spatial and hierarchical context. Available as an open-source Python library and a high-throughput serverless cloud API, Unstructured integrates directly with LangChain, LlamaIndex, and major vector databases to power mission-critical enterprise knowledge retrieval.

Unstructured processes more than 25 document formats through a multi-stage layout detection pipeline. Its vision models detect complex multi-column layouts, rotated text, complex mathematical notation, and embedded charts. For tabular data, Unstructured extracts full HTML and Markdown table structures, ensuring that financial balance sheets and technical specifications retain exact row-and-column relationships when embedded into vector stores. Unstructured includes automated semantic chunking strategies that respect document boundaries (chunk_by_title), preventing context fragmentation and maximizing retrieval accuracy in downstream RAG applications.