ObsessionDB/ClickHouse adds Jev for semantic SQL queries
ClickHouse's aiFilter uses Jev to semantically filter and score agent sessions at about 50 rows per request and 300ms, treating Jev as a database primitive.

We just added Jev support to ObsessionDB/ClickHouse. The interesting part isn’t just “LLM in SQL”, it’s that Jev is fast enough to make semantic queries feel like part of the database workflow. You can analyze AI agent sessions on the go! aiFilter(session, 'was the user frustrated, map('model','jev-1.13.0')) or score frustration, success, intent, exposed credentials, etc. At write time: turn meaning into columns. At read time: ask new questions you never instrumented. ~50 rows/request, ~300ms in our current setup. Feels like a new primitive for AI product telemetry.