DIAL RAG

DIAL RAG works with a wide range of formats including PDF, DOC/DOCX, PPT/PPTX, TXT, code files, and images (JPEG, PNG), ensuring all your information is accessible regardless of how it is stored.

The solution leverages multiple specialized retrieval methods including description retrieval with vision models, multimodal embedding search, semantic text retrieval, and keyword-based algorithms to find the most relevant information across all content types.

DIAL RAG can extract insights from text, tables, images, and charts within your documents, providing comprehensive understanding of complex information that goes beyond basic text analysis.

All responses include citations to source materials, ensuring transparency and trustworthiness while significantly reducing AI hallucinations.

Following the API-first approach, DIAL RAG seamlessly integrates into existing workflows and applications, making it ideal for building enterprise GenAI solutions.

DIAL includes RAG Evaluation Toolkit with intuitive UI and powerful libraries allowing to assess the quality of information retrieval and generation of any RAG-like application based on various metrics, compare performance of different apps side-by-side, visualize results and much more.

DIAL RAG can process massive external knowledge bases, seamlessly integrating with enterprise systems like Confluence and corporate knowledge repositories to create comprehensive information retrieval systems that span your entire organizational knowledge.

Built for business applications, DIAL RAG leverages parallel request processing and GPU optimization, which enables it to scale horizontally, handle massive document collections and growing user demands without performance degradation—delivering rapid insights at any scale.