arXiv:2606.05836v1 Announce Type: new Abstract: Large language models have substantially advanced Text-to-SQL systems, yet applying them to enterprise-scale databases remains challenging. Real-world databases often contain large and heterogeneous schemas, incomplete metadata, dialect-specific SQL syntax, and complex analytical questions that are difficult to solve with a single SQL query. To address these challenges, we propose ProSPy, a Profiling-driven SQL--Python agentic framework for enterprise-scale Text-to-SQL. ProSPy structures the reasoning process into four stages: it first extracts f
The proliferation of large language models and the increasing complexity of enterprise databases necessitate more robust Text-to-SQL solutions that can handle real-world challenges like heterogeneous schemas and incomplete metadata.
This development addresses a critical bottleneck in leveraging LLMs for practical enterprise data interaction, moving beyond theoretical benchmarks to solve real-world data complexity found in large organizations.
The ability of AI to interact with and query complex enterprise databases becomes significantly more practical and efficient, reducing the need for specialized human intervention in data extraction and analysis.
- · Enterprise software companies
- · Data analytics platforms
- · Businesses with large databases
- · AI integration services
- · Traditional database administrators
- · Manual data querying specialists
Enterprise data accessibility and analysis improve dramatically through automated SQL generation.
Reduced operational costs for data-intensive businesses and faster decision-making cycles.
Enhanced overall productivity and new layers of data-driven insights becoming available to non-technical users, further democratizing data access.
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