This framework implements an Orchestrated Test-Time Scaling strategy that combines multiple perspectives to improve Text-to-SQL performance on challenging benchmarks like BIRD. It leverages scalable computation and LLMs to bridge the gap between state-of-the-art models and human expert performance in database querying, achieving 81.67% execution accuracy on the BIRD leaderboard.