Ogment AI - Your AI coworker, in Slack. Just tag @O.
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@O is the ultimate AI coworker that lives natively in Slack.
Tag @O like a colleague, to ask anything or delegate daily tasks in plain English. It connects to 1,000+ tools your business runs on, does work while you sleep, and shares memory and skills across your whole team right in Slack, on any model you choose, including your own.
One-click install, and everyone is AI-enabled in under 5min, not just your power users. Zero friction, maximum adoption.
Replies
How does O manage shared team memory and permissions in Slack so it can collaborate effectively without exposing sensitive information across users or channels?
@satyam_raina Good q, this is core to the design. Two pieces:
Permissions are per employee, and each person's @O mirrors their own access, it can only reach what they're already allowed to reach, so nothing sensitive leaks across users or channels by default. Memory works on two layers: an admin-managed org layer that gives everyone shared base context, and a personal layer that stays private to each user. So you get real collaboration, shared context where it helps, without opening up sensitive work to people who shouldn't see it. Admins keep visibility over the whole thing 🙂
how does the memory and skills sharing actually work across team members, like does everyone see the same context or can you scope it per project?
@fatmaklwx31 Good q! You can scope it, it's not one flat shared brain. Memory works in layers: an admin-managed org layer everyone shares, plus personal memory that stays private to each user. Skills are shareable too, so a workflow built once can be handed to the team or kept to a project. And since permissions are per employee, each person's @O only reaches what they're allowed to, so project-level scoping holds. Shared where it helps, scoped where it matters 🙂
Hey Teo, tagging it like another person on the team is what makes this click for me. Most tools ask everyone to change their habits, and this one meets people where they already are.
@raphael_kamm thanks, that's the whole thesis in one sentence 🙂 Changing habits is where most AI tools quietly die, so we tried to build something that needs zero new behavior, just tag it like you'd tag anyone else. Really appreciate you getting it 🙏
the "@O" name being usable as an actual mention in Slack is such a smart move, makes it feel less like a chatbot bolted on and more like an actual teammate people will naturally ping
@sercannarman - Ha, glad you caught that, the @-mention was a very deliberate choice 🙂 The whole idea was that pinging it should feel identical to pinging a colleague, no new muscle memory.
how does the shared memory actually work across teammates, like does it learn from one person's preferences and apply them to everyone or is it more like a shared knowledge base?
@cemilkavcqivn Good q! It's not one person's preferences bleeding onto everyone. Two layers: personal memory that learns from your own interactions and stays yours, and an admin-managed org layer that acts more like the shared knowledge base, the base context the company wants everyone to have. So your individual preferences stay individual, while the shared stuff is curated on purpose rather than absorbed automatically.
Tagged @O in our team Slack and it actually pulled the report I needed without me opening another tab. The shared memory across teammates is a nice touch too.
@sagldanefyuv thank you so much!
Tagged @O this morning to pull last week's client call notes from Notion and it actually just did it. Being able to share automations across the team without another dashboard is a really nice touch.
@lyasl9qo Imagine how many reports you can delegate to O now :)
Love that @O just slots into Slack like an actual teammate, no separate app or login to babysit. The shared memory across the team is a really thoughtful touch, most AI tools feel like personal gadgets rather than something the whole crew actually adopts.
@helinb3kk Thanks 🙂 That "personal gadget vs something the whole crew adopts" distinction is exactly the line we were trying to cross, an AI tool only pays off when it's the whole team using it, not just one power user.
How does pricing actually work if multiple teammates are tagging @O throughout the day — is it per user, per workspace, or based on the volume of tasks it handles?
@vedatkarabjvvk - it's usage-based, tied to the volume of work @O handles, not per user or per workspace. So multiple teammates tagging it all day doesn't mean seat-by-seat costs, you add the whole team freely and pay for actual tasks done. And since it's model-agnostic, you've got a real cost lever by pointing it at a cheaper one!
Sonnet became super efficient and cost efficient!
Love the breezy integration, making me to automate time-wasting workflows without me having to think twice about it (for me it's scheduling + note aggregation / tracking)!
@ferdinand_legros love it, thanks Ferdi. Good to see you here!