It's not a public facing product, but there was a talk from a team at Alibaba a couple of months ago during CMU's "ML⇄DB Seminar Series" [0] on how they augmented their NL2SQL transformer model with "Semantics Correction [...] a post-processing routine, which checks the initially generated SQL queries by applying rules to identify and correct semantic errors" [1]. It will be interesting to see whether VC-backed teams can keep up with the state of the art coming out of BigCorps.
We have been piloting louie.ai with some fairly heavy orgs that may be relevant: Cybersecurity incident responders, natural disaster management, insurance fraud, and starting more regular commercial analytics (click streams, ...)
A bit unusual compared to the above, we find operational teams need more than just SQL, but also Python and more operational DBs (Splunk, OpenSearch, graph DBs, Databricks, ...). Likewise, due to our existing community there, we invest a lot more in data viz (GPU, ..) and AI + graph workflows. These have been through direct use, like Python notebooks & interactive dashboards except where code is more opt-in where desired or for checking the AI's work, and new, embedded use for building custom apps and dashboards that embed conversational analytics.
Please add Ibis Birdbrain https://ibis-project.github.io/ibis-birdbrain/ to the list. Birdbrain is an AI-powered data bot, built on Ibis and Marvin, supporting more than 18 database backends.
the "check back in a month" soon. I have versions of it that work but I just haven't been satisfied with. also, the major underlying dependency (Marvin) is going through a large refactor for v2. once that stabilizes a bit, I'm going to upgrade to it and that might simplify the code I need a lot
the main problems we see in the space:
1) good interface design: nobody wants another webapp if they can use Slack or Teams
2) learning enough about the business and usually messy data model to always give correct answers or say I don't know.
It would be for people who are not that fluent in SQL. Even as a dev, I find ChatGPT to be easier for writing queries than hand coding them as I do it so infrequently.
Sounds like you are describing a semantic layer. You don't need AI to achieve that, though it is fun when it works. Example of a semantic layer I made below, but there are others out there with more support behind them.
- Minds DB (YC W20) https://github.com/mindsdb/mindsdb
- Buster (YC W24) https://buster.so
- DB Pilot https://dbpilot.io
and now this one