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What is Ragie?

Ragie streamlines the tasks of data ingestion, chunking, and multimodal indexing for both structured and unstructured datasets. By creating direct links to your data sources, it ensures a continually refreshed data pipeline. Its sophisticated features, which include LLM re-ranking, summary indexing, entity extraction, and dynamic filtering, support the deployment of innovative generative AI solutions. Furthermore, it enables smooth integration with popular data sources like Google Drive, Notion, and Confluence, among others. The automatic synchronization capability guarantees that your data is always up to date, providing your application with reliable and accurate information. With Ragie’s connectors, incorporating your data into your AI application is remarkably simple, allowing for easy access from its original source with just a few clicks. The first step in a Retrieval-Augmented Generation (RAG) pipeline is to ingest the relevant data, which you can easily accomplish by uploading files directly through Ragie’s intuitive APIs. This method not only boosts efficiency but also empowers users to utilize their data more effectively, ultimately leading to better decision-making and insights. Moreover, the user-friendly interface ensures that even those with minimal technical expertise can navigate the system with ease.

What is Klee?

Unlock the potential of a secure and localized AI experience right from your desktop, delivering comprehensive insights while ensuring total data privacy and security. Our cutting-edge application designed for macOS merges efficiency, privacy, and intelligence through advanced AI capabilities. The RAG (Retrieval-Augmented Generation) system enhances the large language model's functionality by leveraging data from a local knowledge base, enabling you to safeguard sensitive information while elevating the quality of the model's responses. To configure RAG on your local system, you start by segmenting documents into smaller pieces, converting these segments into vectors, and storing them in a vector database for easy retrieval. This vectorized data is essential during the retrieval phase. When users present a query, the system retrieves the most relevant segments from the local knowledge base and integrates them with the initial query to generate a precise response using the LLM. Furthermore, we are excited to provide individual users with lifetime free access to our application, reinforcing our commitment to user privacy and data security, which distinguishes our solution in a competitive landscape. In addition to these features, users can expect regular updates that will continually enhance the application’s functionality and user experience.

Media

Media

Integrations Supported

Confluence
Google Drive
Microsoft OneDrive
Notion

Integrations Supported

Codestral
Codestral Mamba
Hugging Face
LangChain
Llama
Llama 3
Llama 3.1
Llama 3.3
Ministral 3B
Mistral 7B
Mistral NeMo
Mistral Small
Mixtral 8x22B
OpenAI
Swift

API Availability

Has API

API Availability

Pricing Information

$500 per month
Free Version
Free Trial Offered?

Pricing Information

Pricing not provided
Free Version

Supported Platforms

SaaS

Supported Platforms

Mac

Customer Service / Support

24 Hour Support
Web-Based Support

Customer Service / Support

Web-Based Support

Training Options

Documentation Hub
Online Training

Training Options

Documentation Hub

Company Facts

Organization Name

Ragie

Date Founded

2024

Company Website

www.ragie.ai/

Company Facts

Organization Name

Klee

Company Website

kleedesktop.com

Categories and Features

AI Development

Not specified

MCP Gateways

Not specified

Reranking Models

Not specified

Categories and Features

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