Ragobble
About Ragobble
Ragobble is designed to help researchers and academics optimize their workflow. By creating tailored knowledge bases from various resources, users can leverage AI to generate insights quickly. With easy file uploads and intelligent querying, Ragobble revolutionizes how information is accessed and utilized.
Ragobble offers flexible pricing plans, including a Standard tier at $19.99/month for basic retrieval needs, and an Advanced tier at $29.99/month for more intensive research tasks. Each plan includes distinct Knowledge-Base limits and enhanced AI capabilities, ensuring value for diverse user requirements.
The user interface of Ragobble is intuitive and streamlined, enhancing the research experience. Users can easily navigate through their knowledge bases, upload materials, and interact with AI seamlessly. The thoughtful design promotes productivity and encourages a smooth browsing experience for all users.
How Ragobble works
To get started with Ragobble, users simply visit the Workbench, create a "Knowledge-Base," and upload various resources such as links and files. Once uploaded, users can query the AI for insights based on their knowledge base. This streamlined process makes it easy for users to tap into a wealth of information effectively.
Key Features for Ragobble
AI-Driven Knowledge Bases
Ragobble's AI-Driven Knowledge Bases empower users to store and query diverse types of data. This unique feature allows for efficient research workflows, enabling quick access to insights from uploaded resources. Enhance your information retrieval and analysis capabilities with Ragobble's intelligent tools.
Customizable Upload Options
Ragobble allows users to upload various file types, including articles, videos, and podcasts. This customizable upload feature enhances the platform's flexibility, helping users to gather and organize a wide array of research materials efficiently. Tailor your knowledge base seamlessly with Ragobble.
Retrieval Augmented Generation (RAG)
Ragobble employs the innovative technique of Retrieval Augmented Generation (RAG), enabling AI models to reference up-to-date information efficiently. This technology enhances AI responses by incorporating user-uploaded data, providing accurate and timely insights that are essential for advanced research needs.
