Building the future of intelligent document processing
Ragger is designed to make Retrieval-Augmented Generation (RAG) accessible and powerful for businesses of all sizes. We believe that intelligent document processing should be simple, fast, and privacy-focused.
By leveraging our custom LLM (11LLM) for local LLM inference, we provide a solution that can run entirely on your infrastructure, ensuring your data never leaves your control. Our hosted service offers the same capabilities with enterprise-grade security and support.
Document Processing: Automatically extract and process content from various formats including Markdown, HTML, and plain text.
Embedding Generation: Convert documents into high-dimensional vectors using state-of-the-art embedding models.
Vector Storage: Efficient storage and retrieval of document embeddings using FAISS for fast similarity search.
Semantic Search: Find documents based on meaning and context, not just keyword matching.
RAG Queries: Generate intelligent answers by combining document retrieval with LLM inference using 11LLM.
API-First: RESTful API design that integrates seamlessly with any application or workflow.
Build intelligent knowledge bases that understand context and provide accurate, relevant answers to your team and customers.
Create intelligent assistants that can answer questions from your documentation instantly, reducing support load and improving customer satisfaction.
Find documents based on meaning and intent, not just keywords. Discover insights and connections you never knew existed in your content.
Extract valuable insights from large document collections and make data-driven decisions with confidence.