Imagine asking a database complex questions in plain English and getting perfect SQL queries in return. That’s the promise of Lucy, a new AI framework designed to tackle the challenge of querying large, intricate databases. Traditional AI struggles with the complex relationships within these databases, often missing crucial connections or hallucinating nonexistent ones. Lucy takes a different approach, combining the language understanding of Large Language Models (LLMs) with the power of automated reasoning. First, Lucy identifies the relevant parts of the database based on the user’s question. Then, it uses a constraint solver to map out the logical relationships between those parts, ensuring the generated SQL query is structurally sound and respects the database's constraints. Finally, it constructs a precise SQL query targeting a streamlined view of the necessary data. This three-step process allows Lucy to navigate complex relationships like many-to-many, star, and snowflake schemas, which often trip up other AI systems. Experiments show Lucy outperforms leading zero-shot text-to-SQL methods on benchmarks like the ACME insurance dataset and the BIRD financial dataset, achieving significantly higher accuracy. Lucy isn't just about better SQL generation; it's about democratizing access to data. By simplifying database interactions, Lucy empowers users of all skill levels to unlock valuable insights from their data, paving the way for more data-driven decisions. While challenges remain, such as handling specific types of nested queries, Lucy represents a major step towards seamless communication with complex data systems, opening exciting possibilities for future AI-powered data analysis.