Amplitude AI Feedback: Finally, Signal That Tells You What to Build

I've been talking to product teams for years about what makes building great products hard. Almost no one says "writing the code."

Everyone agrees that the hard part is figuring out what users want.

Feedback comes from everywhere. App store reviews, support tickets, Reddit threads, sales calls, surveys that 3% of people actually fill out, etc. It's all sitting there, full of data that could point your dev work in the right direction.

But here's the thing: there’s no way you can read it all. Your team is already stretched thin. So what happens? You either ignore most of it or you do what everyone does: listen to the loudest voice in the room and hope they represent everyone else. Neither is great.

fixes that. Now available in Amplitude, it analyzes feedback from every channel and surfaces what actually matters to your users.

I designed this solution (originally as ) because when I previously led my product team, I couldn’t get my PMs to talk to users or read their feedback. I spent Friday nights reading thousands of app reviews to make sure we built something people wanted. But as I looked around, product teams everywhere were making the same mistakes. They built features nobody asked for. They missed obvious problems until too late (in churn reports). They had debates about priorities that could have been settled if someone actually knew what customers were saying across all these channels.

The feedback is right there. It can solve problems that every team has. You just can't hear it through the noise.

Why existing tools don't really solve the problem

Here's what I've seen teams try:

• Some use survey tools, but surveys only capture the 3% who bother to respond.
• Others analyze support tickets, which is great, except the person doing the analysis is always biased to surface bugs they want to get fixed. So insights that actually could power innovation get lost.
• There are expensive social listening platforms that still need a team of analysts to make sense of the data.
• Some teams do it manually. They tag and categorize feedback in spreadsheets, which takes weeks, so the information is outdated by the time you finish.

None of these approaches actually solves the problem.

What we built instead

AI Feedback does something different. It pulls comments from everywhere (surveys, support tickets, call transcripts, app reviews, Reddit, Discord, X, G2) and uses AI to quickly read all of it and tell you what actually matters.

Not word clouds. Not generic sentiment trends. Just real answers to the questions you're asking with the right level of detail. For example, it can tell me that last week, 247 users complained about how hard it is to copy and paste something from the app into a deck. It shows me the exact language they used to describe that problem. AI Feedback can even instantly flag this as an implicit request for an export feature.

That's the kind of insight you can actually do something with.

Have you ever seen a feature request that you thought was just one person complaining? What if you knew it had actually been mentioned 47 times this week across six different channels? What if you saw that "confusing onboarding" came up 3x more often in feedback from churned users versus retained ones? This type of analysis is the only way to learn how thousands of your customers actually talk about their problems. Not your marketing language. Theirs.

Combining customer feedback and product analytics

We built something like this as a standalone solution at Kraftful. It showed teams what their users said they should build. It was useful, but it missed an important part of the story. When this type of information lives inside Amplitude, teams can bring together what users are saying with what users are doing.

Consider this example: You read "checkout is broken" in 20 support tickets. That’s something, but how bad is it really? Is this just 20 vocal users, or is this a real problem? Is it getting worse?

With Amplitude, you see the full picture. "Checkout is broken" shows up in feedback, AND you see that checkout conversion dropped 15% this week. You can drill down further to discover that the issue is specific to Safari mobile users. You can pull up Session Replay, watch exactly where they're getting stuck, and quickly fix the issue.

It's not feedback analysis as a separate thing. It's feedback connected to behavior connected to action-leveraging AI, all in one place.

AI Feedback in action: Replit

Forward-thinking teams like Replit are already leveraging AI Feedback to understand precisely what matters to their users and move faster with confidence. By replacing gut feelings with data-driven insights drawn from actual user voices, these teams are shipping features their customers actually want. They’re prioritizing their roadmap to maximize satisfaction and business impact.

“I think the aha moment was truly when I first connected all the sources to AI Feedback, and it did its magic and analyzed everything,” said Amol Jain, Head of Product Engineering at Replit. “That level of visibility was something I just hadn’t had in the past. It was so much work to pull data, look through each of the sources, do the manual work of combining them together—the fact that AI Feedback just did it by clicking buttons? That was fairly magical.”

See for yourself

today! It’s available on every Amplitude plan, including the free one.

Here's why I think you should give it a shot: It takes less than a minute to connect your feedback sources. You'll start seeing insights in minutes. Chances are, you've got feedback piling up right now that could be telling you exactly what to build.