Buster Introduction

Buster leverages a combination of fine-tuned AI models, retrieval-augmented generation (RAG), proprietary ranking systems, ontological graph structures, and reinforcement learning from human feedback to achieve over 98% accuracy in interpreting natural language queries and generating appropriate SQL. It connects directly to an organization's existing databases and builds an understanding of the schema and relationships. Users can then ask questions in natural language, which Buster translates into optimized SQL queries. The system applies strict governance and permissioning to ensure data security. Results are returned as insights, visualizations, or raw data that can be further analyzed or embedded into applications.