- Minds DB (YC W20) https://github.com/mindsdb/mindsdb
- Buster (YC W24) https://buster.so
- DB Pilot https://dbpilot.io
and now this one
[0] "Alibaba: Domain Knowledge Augmented AI for Databases (Jian Tan)" - https://www.youtube.com/watch?v=dsgHthzROj4&list=PLSE8ODhjZX...
[1] "CatSQL: Towards Real World Natural Language to SQL Applications" - https://www.vldb.org/pvldb/vol16/p1534-fu.pdf
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.
See https://github.com/ibis-project/ibis and https://ibis-project.org for more details.
old demo here: https://gist.github.com/lostmygithubaccount/08ddf29898732101...
planning to finish it...soon...
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.
I’m interested in a survey of this field so far and would read it.
I guess the real benefit here is that you don’t need to understand the schemas so the knowledge is not lost when someone leaves a company.
Sort of an abstraction layer for the schemas