Running into issues where the same user reaches out on email first, then switches to Instagram or website chat later. Ends up as separate threads and the team loses context.
Is anyone managing this properly where all conversations map back to one contact with full history?
Trying to consolidate our metric definitions into a proper semantic layer and looking for real feedback from people who've done this.
We've narrowed it down to a few names that keep coming up: Kyvos, AtScale, Cube, and dbt Semantic Layer. The use case is enterprise BI, multiple teams, multiple tools, need consistent definitions and governed access across all of them.
What are people actually using and what's working?
Seeing more MDR vendors position themselves as AI-first and trying to understand what that actually means in practice.
Some seem to just layer AI on top of alerts, others claim to handle investigation more deeply.
If you’ve evaluated or are using any AI-native MDR platforms, which ones are actually worth looking at? Interested in how they perform day to day, especially around signal quality and investigation.
We keep running into the same pattern. Change passes review, tests are green, merges cleanly. Then something breaks in a dependent service a day or two later and the root cause traces back to that PR.
The code was correct. The problem was in how it interacted with the rest of the system. Has anyone found a good way to catch this kind of thing before merge rather than after?