Software teams are drowning in a sea of fragmented issues across GitHub, Discourse and emails. Valuable feedback is often buried under noise. SeaTicket transforms community support by syncing these into a single workspace. What makes it different? Full Context: Bring related issues and documents when solving an issue. AI Agents: Built-in agents autonomously suggest resolutions using existing documents and previous issues. Stop digging for context. Start resolving.
Congrats on the launch. The cross-channel context piece is very real, especially when GitHub, Discourse and email all describe the same issue slightly differently.
The bit I am curious about is where you draw the line between suggesting a resolution and taking action. For example, when the agent has enough confidence, does it only draft the reply, or can it also update GitHub state, close/merge linked issues, or send the customer response? And if it can act, do you model approvals or audit per workspace/customer?
The fragmented issues problem is real. Same question comes in across GitHub, email and Discourse and each one gets handled separately with no shared context. Pulling it all into one place before you respond is the part that actually saves time. Good luck with the launch.
I like the idea that every resolved ticket makes the next one easier. Support history usually just sits there, so turning it into active context is a solid move.
Bringing full context into the resolution flow is the right call. Most support tools surface the ticket but not the relevant prior issues or docs. We've dealt with this fragmentation problem building customer-facing workflows. How does the context retrieval work under the hood? Is it vector search over your issue history, or do you use a graph structure to connect related threads?
Bridging a support ticket to actual GitHub context is the right call. Most ticket tools stop at tagging; getting the agent to trace a user report to a code path changes the quality of the response entirely. We've built similar plumbing in-house and the context assembly is the hard part. How does the agent handle cases where one symptom maps to multiple possible root causes?
The context loss problem is interesting because it’s not just a tooling problem, it’s a relationship problem. Every time a user has to re-explain their issue, the implicit message is ‘we don’t remember you.’ Congrats on the launch!
Hey Product Hunt! 👋
I’m Daniel Pan, Co-founder of Seafile, and I’d like to introduce SeaTicket, which came from a problem we lived with for years.
Through years of community support, we saw how scattered channels turn into lost context and slower teams. So, we built SeaTicket for teams that are tired of losing things, whether that’s time, context, or the trust of the users they’re trying to help.
With SeaTicket, your team finally gets to:
🧠 Focus on solving, not searching
spend your energy on the problem in front of you, not hunting for context across tools
⚡ Move faster without more people
handle more issues, at higher quality, without growing your headcount to match
🔁 Get smarter with every issue you close
your past work actively improves how you handle future problems
👥 Keep everyone on the same page
support, engineering, and product working with full shared context, no handoff friction
🤝 Build real trust with your users
give them visibility, consistency, and fast answers instead of silence
The result: less chaos, more clarity, and a support experience your users actually appreciate.
Try SeaTicket for free with no credit card required.
From Daniel Pan (Co-founder), SeaTicket team
About SeaTicket on Product Hunt
“Al agent that resolves issues across all your channels”
SeaTicket launched on Product Hunt on June 10th, 2026 and earned 117 upvotes and 11 comments, placing #12 on the daily leaderboard. Software teams are drowning in a sea of fragmented issues across GitHub, Discourse and emails. Valuable feedback is often buried under noise. SeaTicket transforms community support by syncing these into a single workspace. What makes it different? Full Context: Bring related issues and documents when solving an issue. AI Agents: Built-in agents autonomously suggest resolutions using existing documents and previous issues. Stop digging for context. Start resolving.
SeaTicket was featured in Productivity (653.5k followers), Artificial Intelligence (470.6k followers) and GitHub (41.3k followers) on Product Hunt. Together, these topics include over 259.8k products, making this a competitive space to launch in.
Who hunted SeaTicket?
SeaTicket was hunted by Jacky Zhang. 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 SeaTicket stacked up against nearby launches in real time? Check out the live launch dashboard for upvote speed charts, proximity comparisons, and more analytics.
Congrats on the launch. The cross-channel context piece is very real, especially when GitHub, Discourse and email all describe the same issue slightly differently.
The bit I am curious about is where you draw the line between suggesting a resolution and taking action. For example, when the agent has enough confidence, does it only draft the reply, or can it also update GitHub state, close/merge linked issues, or send the customer response? And if it can act, do you model approvals or audit per workspace/customer?