Mantle Chat
AI-powered collaboration platform
643 followers
AI-powered collaboration platform
643 followers
Mantle Chat is an AI-powered platform that helps teams communicate, collaborate, solve problems, and work with AI together in one place. It combines team messaging, AI models, agents, file sharing, integrations, and a shared knowledge base, so teams can stay connected and work productively.
Instead of using AI in separate private chats, everyone can chat with teammates and use AI in the same shared workspace.
This is the 2nd launch from Mantle Chat. View more
Mantle Chat
Launched this week
Mantle Chat helps teams boost productivity and adopt AI faster by bringing real-time messaging, AI model chats, autonomous agents, and tool integrations into one shared workspace. Communicate in Discord/Slack-style channels, mention AI agents and models (GPT, Claude, Gemini, Grok, Deepseek) with @ whenever you need help directly in conversations with teammates, build agents, run autonomous tasks together, and connect 30+ tools (Notion, Linear, Gmail and more) your team already uses.
Free Options
Launch Team / Built With
Mantle Chat
Hey Product Hunt community 🙌
I’m Katja, co-founder of Mantle Chat.
We built Mantle Chat because we think AI at work should feel collaborative, not isolated. When AI is part of the team workspace, it becomes more powerful, easier to adopt, and more natural for everyone to use.
How we got here?
We spent time talking to modern teams and noticed one thing: teams are already using AI, but not really together.
They have one tool for team messaging, another set of tools for AI, and a lot of work happening in between. Team communication happens in Slack or Teams, while AI work happens in personal conversations with ChatGPT, Claude, Gemini, and other tools. This creates a few painful problems:
Constant switching between team messenger, AI tools, docs, and tasks
Context gets lost every time people jump between apps
Slack + multiple AI subscriptions quickly become expensive
People use AI quietly, but don’t always share how they’re using it
AI conversations stay private, so teammates duplicate work
The AI has no real context on what the team is working on
Prompts and responses get copied around manually
Non-technical teammates often find AI harder to adopt
Companies want everyone to use AI, but most AI tools still feel individual or developer-focused
The result is strange: AI is everywhere, but teamwork around AI is still fragmented.
So we asked ourselves: what if AI was not a separate tool, but part of the team workspace itself?
What we built?
Mantle Chat is a collaborative AI workspace for teams.
It brings real-time team messaging, AI models, agents, tool integrations, and shared knowledge into one place.
What you can do in Mantle Chat:
Chat with your teammates in Slack/Discord style channels, DMs, and threads.
When you need help from AI, you can mention (@) 60+ models GPT, Claude, Gemini, Grok, DeepSeek, directly inside the conversation.
The AI can respond in the same thread, where the whole team can see it, discuss it, reuse it, and build on top of it.
You can also keep private AI chats when you need to work individually.
Mantle Chat gives teams a shared knowledge base, so uploaded docs, workspace context, and project information can become part of the AI’s understanding. Instead of manually copy-pasting background into every prompt, your team can give AI the context it needs once and use it across conversations.
Teams can also build shared AI agents with custom instructions, knowledge, schedules, and integrations. These agents can run manually, on a schedule, or be triggered by connected tools. (For example, you can create agents for research, PR reviews, meeting notes, analytics reports, customer requests, Linear updates, Stripe events, and more.)
Mantle Chat connects with 30+ tools, including Linear, GitHub, Google Drive, Notion, Slack, and Stripe, so AI can help with real workflows, not just answer questions.
Give the whole team access to AI without IDEs or complicated setup
Reduce the need for separate team chat and AI subscriptions
And honestly, figuring out AI as a team is much more fun and effective than doing it alone.
Is it for you?
Mantle Chat is built for teams that already use AI, or want to start using it together.
For startup teams: Mantle Chat gives you one place to talk, think, plan, and work with AI. You can keep product discussions, customer insights, research, and AI-generated ideas visible to the whole team.
For product and design teams: Mantle Chat helps you brainstorm, summarize feedback, compare ideas, write specs, analyze research, and keep AI outputs connected to the conversations where decisions happen.
For engineering teams: Mantle Chat lets you bring AI into technical discussions, create PR review agents, summarize Linear issues, connect GitHub, and reduce context-switching between tools.
For operations and support teams: Mantle Chat can help automate recurring workflows, generate reports, summarize requests, and create agents that run on a schedule or react to events.
For non-technical teammates: Mantle Chat makes AI feel approachable. You do not need an IDE or developer workflow to use agents, models, and automations. You just work inside chat.
For cross-functional teams: Mantle Chat helps product, design, engineering, operations, support, and leadership stay aligned by keeping conversations, AI outputs, shared knowledge, and workflows in one place.
See it in action:
You can try the interactive demo in the hero section of our homepage:
https://mantle.chat/home
For the Product Hunt community:
You can use Mantle Chat for free, or try the Pro plan free for 7 days with higher limits and all features.
Contact with us:
Website: https://mantle.chat
X / Twitter: https://x.com/MantleChat
LinkedIn: https://www.linkedin.com/company/mantlechat
Community: https://discord.com/invite/STzq94kdDC
Huge thanks to @fmerian for hunting us today, and to the Product Hunt team and community for the opportunity to share what we’ve been building.
Please support us today and drop a comment!
We’d love to read your feedback, any thoughts on how we can make shared AI work better for teams, and how your team is using AI today: together, separately, or somewhere in between.
PicWish
@fmerian @katja_danilina awesome launch Katja! maybe add detailed usage analytics per seat so growth teams can map out which internal ICP are adopting AI fastest.
Mantle Chat
@mohsinproduct That’s a cool idea! Thanks for sharing!
@fmerian @katja_danilina This resonates so much! 🙌 As a solo indie maker, I feel this pain even more — juggling between Slack, ChatGPT, and my dev tools. The 'AI in isolation' problem is real, and solving it at the team level makes total sense. Love the collaborative approach. Upvoted! 🚀
P.S. — Launching iBGremove today too, love seeing fellow makers tackle real workflow problems!
Mantle Chat
@fmerian @katja_danilina Hey Katja, congrats on the launch 👋
The model-agnostic + agents + tool integrations combo is a strong direction. One angle I'd love your take on: I build in the agent-security space (KeepAI, focused on credential/permission layer between AI agents and user apps), and the question I keep hitting is what happens when a shared workspace agent needs to touch tools where different teammates have different scopes.
Concretely: if a "summarize Linear backlog" agent runs on a schedule, is it pinned to the creator's OAuth, fanned out to each viewer's scope at read-time, or running on a workspace service account? Different choices have very different security stories.
Either way - really like the framing of "AI shouldn't feel isolated." That's the gap most tools miss.
Mantle Chat
@arturbrugeman Hi Artur, that's a really good question, I like it. Thank you.
Today Mantle Chat handles this as per-user scoped, not as a workspace service account.
For interactive runs/prompts, tools resolve against the user who invoked the agent. For scheduled/background tasks, they resolve against the user who created the task or trigger. So if I create a scheduled “summarize Linear backlog” agent, it runs with my connected Linear credentials and writes the result back into Mantle. Teammates may be able to read that output depending on workspace/channel access, but they do not inherit my Linear connection. If a teammate runs the same agent manually, Mantle uses their own connected accounts. If they have not connected Linear, the Linear tools are not available for that run and they need to OAuth-connect to it themselves.
We don’t re-check every viewer’s app permissions when they read the result, and we don’t have shared workspace-owned app connections yet. Admins can manage the agent, but they can’t use another user’s connected accounts.
Long term, I agree this becomes a first-class product/security layer: explicit delegation, workspace-owned connections, audit logs, approval flows, and per-tool permission boundaries. That’s where KeepAI’s framing feels very relevant and complementary, right? I took a glance at your product and your repo, looks good so far, keep on going!
CatDoes
Love the "@ the model in the same channel as your teammates" framing, that's the part Slack-with-a-bot-bolted-on always got wrong. Curious how you're handling context isolation when multiple agents are working on tasks in parallel in the same channel? Does each agent get its own thread, or do they share the channel history?
Mantle Chat
@mahdi_nouri Thanks, that’s a really good question.
We support both patterns. In channels, when you "@ an agent or model", it can work with the shared channel context, so teammates and agents are aligned around the same discussion. And when a task needs more focus or isolation, you can turn any specific message into a thread from the channel. Threads have their own separate context/history, so multiple agents can work in parallel without mixing up task-specific details.
Shared AI threads are the right move. The problem with private AI sessions is that your team's best prompts and outputs never get socialized. At RetainSure we've got CS reps doing similar AI-assisted tasks in silos, so we lose institutional knowledge constantly. Do you have any versioning or replay for how an AI-assisted decision was reached in a shared channel?
Mantle Chat
@dhiraj_patel5 Thank you for your great question! Right now, we have replays in shared chats and we don’t have versioning, but we agree it’s an important direction for shared AI workflows.
Shared AI context across a team is way better than everyone running isolated prompts. Mantle Chat looks like it could cut down a lot of duplicated work in growing teams. We've been building in the customer success for ops-heavy SaaS teams space at RetainSure, and Mantle Chat touches on something we think about a lot. How does it handle context isolation between different projects?
Mantle Chat
@shivam_jaiswal21 Thank you for your comment! We’ve thought about it: Mantle Chat is structured around organizations, where teams can create separate workspaces for different needs: for example, departments, projects, or teams. This helps keep shared context useful without mixing unrelated project knowledge. It’s also easy and intuitive to switch between workspaces using the bottom panel, which was specifically designed for this workflow. You can create as many workspaces as you want.
One underrated benefit here is institutional memory . Shared AI conversations are way more valuable than knowledge trapped in private chats.
Mantle Chat
@gabriel_brooks1 Thanks you! Definitely, private AI chats are useful for individual productivity, but shared AI threads turn that productivity into team knowledge.
Does Mantle keep a persistent shared context across sessions — so the AI understands decisions the team made in previous conversations — or does each new session start fresh? That long-term memory layer seems like the thing that would make it genuinely more useful than just adding AI to a Slack channel.
Mantle Chat
@sunnyallan Thanks for your question!
Mantle Chat doesn’t treat every new session as completely fresh, if you add context to the workspace knowledge base.
Teams can add shared files, instructions, and context at the workspace level, and agents can use that across conversations. We also support agent-level knowledge bases, so each agent can have its own specialized context.
We’re also working on the desktop app and local file access, so the context layer can go beyond just what was said in a single channel or thread.
Love the shift from isolated chat to collaborative, event-driven agent workflows. When your agents are running in 'Flows' and interacting autonomously with multiple external APIs like Stripe or GitHub, how does the system handle state persistence and error recovery? If an API rate-limits the agent halfway through a scheduled task, I'm curious if the agent can dynamically adapt and retry, or if it hard-fails back to a human