[cmrdporcupine]

RelationAI effectively has a kind of datalog as a commerical product, and it runs inside Snowflake (something they implemented since I worked there). It's marketed as "graph" database but they mean by that that they have modeled graphs as binary relational data, really. It's a purely relational system, with a friendly query language ("Rel") which is vaguely Datalogish, but a bit more flexible.

The key thing with them is it's designed for querying very large cloud backed datasets, high volumes of connected data. So maybe it's not as relevant here as I originally suggested.

Re: marketing ... much of their marketing has shifted over the last two years to emphasizing the fact that it's a plugin thing for Snowflake, which wasn't their original MO.

(There's an CMU DB talk they did some years ago that I thought was pretty brilliant and made me want to work there)

My proposal about a datalog (or similar more high level declarative relational-model system) being useful here has to do with how it shifts the focus to logical propositions/rules and handles transitive joins etc naturally. It's a place an LLM could shove "facts" and "rules" it finds along the way, and then the system could join to find relationships.

You can do this in SQL these days, but it isn't as natural or intuitive.