Business reviews depended on analysts translating recurring questions into SQL, assembling the results, and rebuilding the same reporting views. Natural-language analytics could shorten that loop, but enterprise adoption required more than a model demo: generated queries had to fit existing data access, review, and compliance rules.
GPT-based Text2SQL and Python analytics tools for quarterly business-review workflows. I led the product through engineering, legal, and compliance, turning an emerging model capability into a production workflow rather than a standalone experiment.
The model was only one dependency. The product also needed a clear boundary between generation and execution, a reviewable output, and enough transparency that users could trust the analysis without treating fluent text as proof of correctness.
The tools cut quarterly business-review reporting time by 70% and established an early path for shipping generative AI inside an enterprise environment.