Lessons from building an AI data analyst

> The "Short Story" section definitely matches my experience at most companies, startups and bigger non-tech companies alike: They already have more data than they're aware of and know what to do with, and understanding what they have is the starting point before most analysis can be done.

exactly, most of them are concerned about the data they don't have, while in practice they do have a lot to generate good insights.

> Glad I read the post as I hadn't heard of Malloy before. Excuse me if I missed the answer to this, but: How much do you as Findly/Conversion Pattern implement the Semantic Layer on behalf of your users (and if so, I assume you have some process for auto-generating the Malloy models), or do your users have to do something to input the semantics themselves?

We do have an automatic semantic layer generation framework which works as a great starting point, but for the generic case you still have to manually edit / improve it based on the customer's internal context. User's can edit themselves in our UI too, but it usually requires some level of help from us.

We do have a vertical product for commodity trading and shipping: https://www.darlinganalytics.ai/ -> in that case the semantic layer is much more well defined, which makes setup way easier.