Ragie

Ragie is a fully managed Retrieval-Augmented Generation (RAG) platform designed for developers to build advanced AI applications. It streamlines the process of data ingestion, chunking, and multimodal indexing for both structured and unstructured data, enabling efficient information retrieval and processing.

Key features:
- Data Ingestion and Connectivity: Supports direct file upload via API and offers built-in connectors for popular data sources such as Google Drive, Notion, Confluence, OneDrive, and Salesforce, with automatic data syncing to ensure up-to-date information.
- Multimodal Data Support: Processes various data types including text, PDFs, images, and PowerPoint presentations.
- Advanced Data Processing: Performs automatic chunking of ingested data, vector embedding using multi-lingual LLMs, and multiple vector indexing (chunk, summary, keyword).
- Sophisticated Retrieval Capabilities: Offers semantic search queries, LLM re-ranking for improved accuracy, summary index for quick overviews, entity extraction, flexible vector filtering, hybrid semantic and keyword search, and top-k relevant chunks and documents retrieval.
- Scalability and Integration: Provides seamless integration with agentic systems and highly scalable vector database storage.
- Developer-Friendly Interface: Requires no code for basic setup, offers a simple API for data upload and retrieval, and provides comprehensive documentation.
- Transparent Pricing: Features a straightforward pricing model with a free 30-day Pro Trial.

How it works:
1. Users upload files directly through Ragie's API or connect to supported data sources.
2. Ragie automatically processes the ingested data, chunking and embedding it into vectors using multi-lingual LLMs.
3. Users can query the processed data using Ragie's retrieval API.

Integrations:
- Google Drive
- Notion
- OneDrive
- Confluence
- Salesforce
- Gmail (coming soon)
- Jira (coming soon)

Use of AI:
Ragie utilizes generative AI through multi-lingual LLMs for vector embedding and retrieval optimization. This AI-driven approach enables efficient processing and accurate retrieval of information from large datasets.

Target users:
- Developers building AI applications
- Companies dealing with large amounts of unstructured data
- Industries such as Technology, Finance, Healthcare, Education, Media and Entertainment, and Research and Development

How to access:
Ragie is available as a web-based service with API access for developers. Users can sign up for the service and integrate it into their applications using the provided API.

Software availability:
Ragie is a proprietary, web-based service and is not open source.