AI21 Maestro's Structured Retrieval-Augmented Generation (S-RAG) addresses the limitations of traditional and embedder-based RAG systems in handling complex enterprise queries by transforming unstructured documents into structured, query-aware representations. This approach enhances the accuracy, reliability, and transparency of responses by using a hybrid architecture that combines structured and embedder-based retrieval, resulting in up to 60% improved accuracy and near-perfect recall. S-RAG is particularly effective for enterprises by automatically inferring or allowing users to define schemas, which enables precise analytical operations required in compliance, reporting, and mission-critical workflows. By converting documents into structured records with consistent formatting, AI21 Maestro ensures comprehensive data coverage and allows for precise SQL queries, thus overcoming the probabilistic limitations of traditional RAG systems and providing dependable, auditable answers. This innovation transforms enterprise data into a reliable decision-making engine, facilitating automation and risk mitigation in high-stakes environments.