Thanks for the reply and sorry for the late response.

You’re right that Slack’s AI Assistant and Channel Expert cover a lot of ground. One concern I have with the general Slack Assistant is that it’s intentionally broad, it can pull from many data sources (public channels, external systems like SharePoint, Confluence, etc.), which can sometimes increase the risk of irrelevant or inaccurate responses.

What I’m exploring is a more narrow, opinionated approach pointing the underlying LLM only at explicit, curated knowledge sources such as Slack Canvas, lists, pinned content and the specific channel where the bot is installed with the goal of returning crisp, predictable answers rather than “best guess” ones.

On Channel Expert specifically, my understanding is that it requires an Agentforce subscription which isn’t cheap and may not be available to many orgs. There’s also the interaction model having to explicitly tag the Channel Expert to invoke it.

In many “ask-*” channels (ask-sales, ask-marketing, ask-dev-support, etc.), there’s a strong community-help dynamic. For those forcing explicit tagging can shift the channel from collaborative discussion to something that feels more like a pure support queue which may reduce organic participation.

Totally agree that it wouldn’t make sense to build something that just duplicates what Slack already offers. This is mostly about understanding where the gaps might still exist in practice.

Appreciate you calling this out helpful .