Designed and developed a Conversational Text-to-SQL AI system that enables non-technical users to query financial and sales databases using natural language. The system converts user questions into SQL queries using Large Language Models and retrieves insights from structured databases, enabling faster and more accessible business analytics. Key Features: • Built a natural-language-to-SQL agent using LangChain and GPT-based LLMs • Enabled users to ask finance and sales questions in plain English • Automatically generated optimized SQL queries for structured databases • Implemented query validation and error handling for reliable database access • Added automated visualization using Matplotlib and Seaborn for data insights • Achieved ~95% query success rate in simulated enterprise finance datasets Architecture Highlights: • LLM-based prompt engineering for Text-to-SQL translation • LangChain agent orchestration • SQLite structured financial datasets • Automated analytics and visualization layer Business Impact: This system demonstrates how LLM-powered analytics assistants can democratize data access by allowing business teams to retrieve insights without SQL knowledge. Tech Stack: Python, LangChain, GPT/LLMs, SQL (SQLite), Pandas, Matplotlib, Seaborn