Kai Resolve by Gleap

Support that does the digging for you.

Customer Success

Developer Tools

Artificial Intelligence

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Congrats Isabella and the Gleap team. Having the agent read the session recording and logs before a ticket ever reaches a human is the step most support bots skip. On the 75% figure, how do you count a resolution when the customer simply stops replying, and does a likely-bug handoff to engineering count as resolved or as escalated?

Curious if multiple customers report the same bug in different ways, can Kai correlate those tickets to one investigation?

Hi all, Hussein here, I lead Customer Success at Gleap 👋

What I keep hearing from second-level support teams: most of their day goes into tickets that just need digging. Checking logs, rewatching recordings, asking the customer for one more detail. The tricky, important cases end up waiting behind all of that.

Kai Resolve turns that around. It does the digging and handles the routine investigations, so your second-level team can finally focus on the tickets that really need them: the complex bugs, the key accounts, the decisions only a person should make.

Based on our Gleap data, Kai Resolve resolves 75% of all tickets automatically.

If you'd like to see it on one of your own tickets, I'd be happy to set it up with you: https://cal.gleap.ai/hussein

Reading the screen recording and the logs before answering is what makes this interesting, since most support tools just paraphrase the help center and call it AI.

Hey Product Hunt 👋 Florian here, CTO at @Gleap .

@isabella_salzmann covered the why, so here's the part I'm most excited about as an engineer: how Kai Resolve investigates.

When a tricky ticket comes in, it works like a good developer would: → follows the click trail and watches the session replay → reads the console and network logs to see what actually failed → checks the account data for anything unusual → reads your code and points to the exact file and line

The more it can see, the deeper it goes. Connect your monitoring or internal tools via MCP, and it can trace issues that never show up on the user's screen.

Send it the bug your team has been avoiding for weeks, and tell me how it did. I'm here all day for any questions about how it works under the hood 🔍

Hey Product Hunt 👋 Tobi here, co-founder of Gleap.

We’ve been building support tools for years, and one thing kept bugging me: the hardest part of support was never writing the reply. It was figuring out what actually went wrong. Teams spend hours jumping between logs, recordings and code just to understand one ticket, and the customer is left waiting the whole time.

AI got really good at answering questions, but most “AI support” stops right there. We wanted something that does the part that actually hurts: the investigation.

That’s Kai Resolve. It looks into every ticket like an experienced teammate would, answers what it can, and hands the rest to your team with the cause and the evidence ready. And the more context you give it (your docs, your code, your past tickets), the more it solves on its own.

For me, this is the support tool I always wished we had.

I’d love to hear from everyone running support: what’s the part of the digging that takes your team the longest? 🙌

Hey, Luca here, software engineer at Gleap 👋

I built the setup and settings for Kai Resolve and worked a lot on answer quality. What surprised me was how often fixing one case would quietly break another. Testing every change against all our scenarios slowed us down, but it’s why I trust the answers now.
If you try it and something feels off, please tell me here. I’d much rather hear it today than read it in a ticket next week 😄

Congrats on the launch, Gleap team! At MailerCloud (mailercloud.com) we deliver 6 Billion+ emails every month, and our support team runs on Gleap. The slow part of every ticket is the digging: logs, account history, figuring out whether it’s a bug or a setup issue. Having Kai do that before a human picks it up is exactly what we need. Excited to roll it out!

Telling the customer once their fix is live is the bit I like here. Easy for that last message to get lost after the engineering work is done.

Hey Product Hunt 👋

Lukas here, co-founder of Gleap.

I’ve been building software for most of my life, and the part I always hated was the gap. A customer reports a bug, it becomes a ticket, the ticket becomes a Jira issue, and weeks later someone ships a fix. The customer who reported it never finds out. The feedback loop just… never closes.

That’s why I’m so excited about Kai Resolve. It’s the first time support and engineering feel like one team.

Here’s what happens when a hard ticket comes in:

🔍 Kai Resolve investigates it like a developer would: logs, click trail, session replay, and the actual code in your GitHub repos
✅ It tells your team what’s going on (bug, feature request or setup issue) and lists the files it read, so you can check its work
🛠️ For a real bug, it prepares a coding task. One click hands it to Kai Code, Claude Code, Codex or Cursor, and you get a pull request to review

💬 Once the fix is live, the customer hears back on the same ticket they opened - automatically.

What I love most is that last step. The person who took the time to report the problem actually hears “we fixed it, thank you.” I think that’s what turns users into fans.

Your engineers stay in control the whole way: they review every PR and decide what ships. Kai Resolve just takes on the digging and the back-and-forth that used to eat our week. We run our own support on it every day, and it’s honestly changed how we build Gleap.

I’d love your feedback, especially from devs and founders: how does the loop from customer report to shipped fix work on your team today?

I’ll be here all day 🙌