@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.
Hey Product Hunt 👋 I'm Teo, CEO and co-founder of Ogment.
Most companies are told to "adopt AI", but in practice that means asking non-technical people to learn new apps, prompt engineering, and workflow builders. There's one thing we've learned over the past two years offering AI to companies: any bit of friction kills adoption. A few power users push through, true, but everyone else gets left behind.
You've probably seen the wave of Slack-native agents shipping lately, Claude Tag included. They're strong for technical teams who want to own the setup. We went the other way: every employee gets their own AI coworker, on any model you choose (including your own local LLM), with zero setup.
We built @O to attack that friction head-on. The bar we set ourselves: using an AI agent should be as easy as tagging a colleague. So that's exactly how it works, you tag @O in Slack, in plain English, and it gets the work done. No new app, no prompt craft, no workflow builder. When the friction drops to zero, adoption is maximized, the whole team starts using AI super naturally, from day one.
This is sick. What are your top 3 use cases? Just tried the sales use case and it is amazing. Great job.
The shares-memory-and-skills-across-your-whole-team part plus running automations while you sleep is the combination I would want to pin down before rolling it out. When @O touches one of those 1,000+ tools, is the connection scoped per-user so it acts with my permissions, or is it one workspace-level token everyone shares? And the shared team memory: does that live in your infra, or can I point it at our own store or model like the including-your-own line suggests?
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)!
Tool-calling is the sneaky failure with any bring-your-own-model setup. Providers each format function calls differently, so a harness tuned on one model's schema quietly drops args or mis-picks tools when you point it at a cheaper endpoint, and it still reads as working. That's the bug that ate the most of our debugging time building agent infra. How much of the agent quality actually holds when someone swaps their own model in versus your default?
Hey Product Hunt 👋 I'm Amaury, cofounder of Ogment, and I'm super proud to share what we've been building.
We kept seeing the same pattern: people are excited about the AI revolution, they want that full "agentic" experience, but then reality hits. The friction to install and maintain agents a la OpenClaw or Hermes is actually pretty high, esp. for less technical people. Not even mentioning the security or the team collaboration aspect...
So we built O from the ground up for Slack-native teams, where a ton of context already lives. Your conversations, your decisions, your files, your workflows: it's all right there. And the best part? There's zero friction to onboard, just tag @O in Slack.
Here's the thing that surprises people most: we don't really have a dashboard. You can do it all from Slack. Connecting your systems (Gmail, Linear, Stripe), creating a custom skill or sharing it with your team, setting up a recurring job... just ask your agent, like you would a colleague.
And it's proactive. Ogment doesn't just wait for instructions. It constantly spots automation opportunities and surfaces them to you, without you even having to ask.
My personal favorite skill with O? It handles my post-product meetings end to end. It pulls the Granola transcript, reads my backlog in Linear, checks strategy docs in Notion, grabs context from emails, and outputs a clear list of feature requests, bugs, market insights, and a backlog update proposal. What used to take me 45 minutes to do properly is now done in 1 minute.
We're incredibly excited to get this into your hands. Can't wait to see what you build with it!
Model-agnostic is a strong claim since reasoning quality varies a lot across models. Can @O route different task types to different models, cheap one for routine CRM updates, stronger one for judgment calls, or is it one model per workspace?
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?
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.
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.
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.
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?
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
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.
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?
tagged @O to summarize a long thread and it actually pulled the key decisions plus action items in seconds, way cleaner than the bots we tried before.
How does O manage shared team memory and permissions in Slack so it can collaborate effectively without exposing sensitive information across users or channels?
About Ogment AI on Product Hunt
“Your AI coworker, in Slack. Just tag @O.”
Ogment AI launched on Product Hunt on July 7th, 2026 and earned 156 upvotes and 124 comments, placing #9 on the daily leaderboard. @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.
Ogment AI was featured in Slack (72.2k followers), Productivity (655.5k followers) and Artificial Intelligence (472.9k followers) on Product Hunt. Together, these topics include over 249.9k products, making this a competitive space to launch in.
Who hunted Ogment AI?
Ogment AI was hunted by KP. A “hunter” on Product Hunt is the community member who submits a product to the platform — uploading the images, the link, and tagging the makers behind it. Hunters typically write the first comment explaining why a product is worth attention, and their followers are notified the moment they post. Around 79% of featured launches on Product Hunt are self-hunted by their makers, but a well-known hunter still acts as a signal of quality to the rest of the community. See the full all-time top hunters leaderboard to discover who is shaping the Product Hunt ecosystem.
Want to see how Ogment AI stacked up against nearby launches in real time? Check out the live launch dashboard for upvote speed charts, proximity comparisons, and more analytics.
KP
Hey Product Hunt 👋 I'm Teo, CEO and co-founder of Ogment.
Most companies are told to "adopt AI", but in practice that means asking non-technical people to learn new apps, prompt engineering, and workflow builders. There's one thing we've learned over the past two years offering AI to companies: any bit of friction kills adoption. A few power users push through, true, but everyone else gets left behind.
You've probably seen the wave of Slack-native agents shipping lately, Claude Tag included. They're strong for technical teams who want to own the setup. We went the other way: every employee gets their own AI coworker, on any model you choose (including your own local LLM), with zero setup.
We built @O to attack that friction head-on. The bar we set ourselves: using an AI agent should be as easy as tagging a colleague. So that's exactly how it works, you tag @O in Slack, in plain English, and it gets the work done. No new app, no prompt craft, no workflow builder. When the friction drops to zero, adoption is maximized, the whole team starts using AI super naturally, from day one.