Provides a fully managed Retrieval-Augmented Generation (RAG) service that enables developers to integrate and process structured and unstructured data from sources like Google Drive, Notion, and Confluence using APIs and SDKs. Automates data ingestion, chunking, indexing, and retrieval with features like LLM re-ranking, hybrid search, and entity extraction, reducing development time from months to weeks while ensuring accurate, context-rich AI outputs.
Funding
$5.5M raised to dateRaised to date based on public sources. This may differ from the amount the company actually raised and is based only on what is publicly available on the internet.
Founders
Product
Problem
Building Retrieval-Augmented Generation (RAG) applications requires significant engineering resources to integrate and manage data from various sources, implement complex data processing pipelines, and ensure accurate AI outputs. Developers face challenges in connecting to diverse data sources, chunking and indexing data, and implementing advanced retrieval techniques.
Solution
Ragie provides a fully managed RAG-as-a-Service platform that simplifies the development of AI applications by automating data ingestion, chunking, indexing, and retrieval. The platform offers pre-built connectors for popular data sources like Google Drive, Notion, Confluence, Sharepoint, Backblaze, Zendesk, and Intercom, enabling seamless integration of structured and unstructured data. Ragie's advanced features, including LLM re-ranking, hybrid search, entity extraction, and summary index, ensure accurate, context-rich AI outputs while reducing development time. Developers can leverage easy-to-use APIs and SDKs to quickly build and deploy RAG applications without managing complex infrastructure.
Target Audience
Ragie is designed for AI developers and product teams building internal chatbots, enterprise SaaS solutions, and other applications requiring context-aware AI capabilities.
Features
- Pre-built connectors for data ingestion from Google Drive, Notion, Confluence, Sharepoint, Backblaze, Zendesk, Intercom, and more
- Automatic data syncing to keep RAG pipelines up-to-date
- Automated data chunking and embedding into vectors using multilingual LLMs
- Vector storage in a scalable vector database with vector, summary, and keyword indexes
- Retrieval API with LLM re-ranking, summary index, entity extraction, and hybrid semantic and keyword search
- Support for audio and video RAG with multilingual transcription and precise timestamps
- Open-source developer tools, including Ragie MCP Server, CLI, and Promptie
- Integration with LangChain via langchain-ragie
- Base Chat: Open-source chatbot reference application