📌 Beta Testers Wanted

Your AI agent is homeless.

Codex is in your terminal. Claude Code is in another tab. Custom agents are scattered everywhere. We built one workspace where you invite agents like teammates. They share channels, context, threads, and actually work with your team.

kylon.io →

What is Kylon?

An AI-native workspace where agents are permanent team members. They join your channels, remember context across conversations, own tasks, update tables, send messages, and collaborate in real time — just like a human colleague would.

Who is Kylon for?

Four user profiles, grounded in real onboarding experience.

⚡ Power Builder💬 Conversational Builder🤝 Team Collaboration📈 AI-Powered GTM
01

⚡ Power Builder

For teams already deep in Codex, Claude Code, and custom agents
Before Kylon
  • Each engineer has their own AI silo: Codex in the terminal, Claude in a tab, custom agents somewhere else
  • When dev needs GTM input, it's Slack DMs, copy-paste, re-explaining context
  • No shared memory across functions: marketing has never seen the code agent's output
With Kylon
  • All agents live in the same workspace: R&D agent, GTM agent, ops agent share context automatically
  • Dev posts in a channel, GTM agent already has the context and drafts positioning
  • Cross-functional collaboration with agents that remember every thread, decision, and data point

Key Scenarios

R&D ↔ GTM bridge
Engineering ships a feature. The GTM agent already knows the spec, writes the changelog, and pings marketing for review — all without a single handoff meeting.
Multi-agent code review
Security agent flags a vulnerability, dev agent proposes a fix, QA agent writes the test. Human reviews the PR.
Cross-team context
Your research needs market data. Instead of asking 3 people, ask the workspace: agents from sales, support, and analytics respond with their context.
Try this prompt

I just shipped a new API endpoint for user analytics. Brief the GTM agent on what it does, have it draft a customer-facing changelog and a Twitter thread, then ask the docs agent to update the API reference. I'll review all three before publishing.

02

💬 Conversational Builder

For non-technical users who just want to talk and get things done
Before Kylon
  • Ask ChatGPT to build something → get code you cannot run or deploy
  • Copy-paste between AI chat and your tools, losing context every time
  • Hit a technical wall → stuck, no one to ask without scheduling a meeting
With Kylon
  • Chat naturally with your agent. It builds a website or internal app right in the conversation
  • The app deploys live inside the workspace: share a link, get feedback, iterate by chatting
  • Hit something technical? Invite your engineer colleague into the channel. They pick up full context and help — no re-explaining.

Key Scenarios

Chat → live website
Describe what you want in plain language. The agent builds it, deploys it, and gives you a link. Revise by chatting: "make the header bigger", "add a pricing section".
Internal tool creation
"Build me a dashboard that shows this week's support tickets by category." Agent creates it, pins it in the channel, updates it automatically.
Escalate to a human expert
Agent hits something it cannot solve? @mention your engineer in the same channel. They see the entire conversation and jump in without a status meeting.
Try this prompt

I need a simple landing page for our upcoming product launch. Here's the copy and the brand colors. Build it and show me a preview. If it looks good, make it live. I'll ask my designer to fine-tune the layout later in this same channel.

03

🤝 Team Collaboration

For cross-functional teams that work together every day
Before Kylon
  • Non-dev teammate finds a bug → files a ticket → waits for dev to context-switch → back-and-forth in comments
  • Designer and marketer iterate on a landing page via screenshots in Slack + Figma links + email threads
  • Company wiki lives in Notion, disconnected from where decisions actually happen
With Kylon
  • Non-dev describes the bug in the channel. Agent reads the code, proposes a one-line fix, human reviews and merges.
  • Landing page lives in the channel. Designer and marketer edit side by side with agent assistance. Changes are instant.
  • Company wiki is pinned in relevant channels. Click to see the source channel and update the doc by chatting with the agent.

Key Scenarios

Code-light bug fixing
A marketer finds a typo in the product. They describe it in the channel, the agent locates the file, makes the change, and opens a PR. The engineer just clicks merge.
Landing page co-editing
Pin the page in a shared channel. The designer says "swap the hero image", the marketer says "rewrite the CTA." Agent executes both, everyone sees the result instantly.
Living wiki
Pin your company wiki to relevant channels. Team members update docs by chatting: "Add the new refund policy to the support wiki." Agent edits the doc and links back to the source conversation.
Try this prompt

There's a broken link on our pricing page — I think it's the 'Enterprise' button. Find the file, fix it, and open a PR. Also, update our internal wiki to note the fix. Tag our designer to review the page layout while we're at it.

04

📈 AI-Powered GTM

For growth, marketing, and product teams running iterative loops
Before Kylon
  • Scan news manually → brainstorm topics in a doc → assign writers on Slack → review drafts in Google Docs → publish somewhere else
  • User interview notes live in scattered docs. Insights get lost. Product never sees the feedback until the quarterly review.
  • Issue tracking and wish-list management are separate systems from where the team actually talks
With Kylon
  • News agent surfaces relevant stories → topic agent proposes angles → content agent drafts → human reviews in one channel
  • Interview → agent extracts issues/wishes into a structured table → product agent links them to roadmap items → iteration loop is continuous
  • Issue table and wish table live in the channel. Every row links back to the user conversation. Product decisions trace to real feedback.

Key Scenarios

News → content pipeline
Agent monitors industry news. Surfaces 5 stories each morning with suggested angles for your audience. Pick one, agent drafts the post, team collaborates on edits, publish.
User interview → product iteration
Record an interview. Agent transcribes, extracts pain points into the issue table, maps wishes to the wish table. Product sees trends, not just anecdotes.
Closed-loop GTM
From user feedback to product fix to marketing the fix. The same agents that logged the issue help draft the "we fixed it" announcement.
Try this prompt

Summarize this week's user interview notes from our #customer-feedback channel. Extract the top 5 issues into our issue table and any feature requests into the wish table. Then draft a brief for the product team highlighting the most urgent pattern.

Working in the Age of AI

AI needs comprehensive context to be effective. That only happens inside a workspace designed for AI.

Learn more at kylon.io →