Sort of. Having perfect data engineering is a requirement if you want to connect an LLM straight to your data warehouse. For real world scenarios, you need a way to add context over time (including examples of how to answer questions from messy data). The same way a new team member would need to be on-boarded the tool needs to learn the context of the data and business logic, store it under supervision from an admin and be able to retrieve it in generating the SQL.
Personally I think that's a great response. Continuous schema mapping, gotcha-patching, and formalization of undocumented knowledge is imperative, and this can be solved by engineers using an ongoing process. Kudos on the launch.
I did an NL-to-SQL startup, but now I think is a much better time to do this.
This only works if you have perfect data engineering. But if you have perfect data engineering, point and click tools like Looker will work too.