Ragie

Ragie is a fully managed RAG-as-a-Service designed for developers to streamline the ingestion, chunking, and multimodal indexing of structured and unstructured data. Ragie offers simple APIs and SDKs, seamless integration with popular data sources like Google Drive, Notion, and Confluence, and built-in advanced features like summary indexing, chunk reranking, flexible vector filtering, and hybrid semantic and keyword search—helping your application deliver state-of-the-art generative AI.

Building production applications using RAG can be very tedious. Developers must connect and sync multiple data sources, extract meaningful data from various file formats, implement evolving techniques for chunking and retrieval, build a scalable and resilient data processing pipeline, avoid hallucinations, and ensure content accuracy. Using open-source frameworks can be time-consuming and often results in brittle applications. Originally developed for Glue, Ragie solves this by providing a fully managed RAG-as-a-Service platform.

Ragie is ideal for developers who want to build AI applications that leverage their own data for accurate and relevant outputs. Whether you're working on internal chatbots, enterprise SaaS products, or any application requiring context-rich AI capabilities, Ragie is perfect for teams wanting to avoid the complexities of setting up a RAG pipeline. From startups needing a quick solution to enterprises requiring advanced data management, Ragie dramatically speeds up time to deployment.

With Ragie, you can easily connect your AI application to your data using simple APIs. Or, connect directly to popular data sources like Google Drive and Notion, with built-in OAuth integration. Automatic syncing keeps your data up-to-date, ensuring your application delivers accurate and reliable information. Ragie handles everything from text and PDFs to images and PowerPoint presentations. After uploading your data, Ragie automatically chunks and embeds it into vectors using the latest multilingual LLMs. These vectors are then stored in a highly-scalable vector database, capable of handling increasing amounts of data and queries over time without slowing down. Out of the box, Ragie builds vector, summary, and keyword indexes, to help organize information and optimize the retrieval process. Finally, use Ragie’s retrieval API to get the most relevant chunks. Built-in advanced features like LLM re-ranking, summary index, entity extraction, and hybrid semantic and keyword search ensure your RAG pipeline delivers the most accurate and relevant results to your AI application.

Ragie was built specifically for developers, offering a streamlined DX. With easy-to-use APIs, SDKs, and seamless connectivity to data sources like Google Drive, Notion, and Confluence, and more, Ragie helps you deploy in minutes instead of months. Built-in advanced features such as summary index, entity extraction, and hybrid semantic and keyword search allows developers to focus on building their applications, instead of worrying about infrastructure.

Ragie protects your data with bank-grade AES-256 encryption for storage and TLS encryption for transmission, with CASA, GDPR, and CCPA certification. SOC2 Type II certification is coming soon. For more information and to gain access to our trust center, please contact support@ragie.ai.

Ragie’s straightforward pricing aligns with the way apps are developed, deployed, and scaled. Ragie includes a free tier for developers to get started building their applications, a Starter tier for small projects, a Pro plan for production, and Enterprise plan for scale. For more information, visit our Pricing page.