Launching today
Audyr
AI captures feedback and tells you what to build next
42 followers
AI captures feedback and tells you what to build next
42 followers
Stop guessing what users want. Audyr's AI captures feedback through conversational widgets and integrations such as Intercom, merges duplicates automatically, and tells you exactly what to build next. Used by curious product teams.
Congrats on the launch! I feel deduplication is the feature everyone looking for feedback tools wants to see, glad to see you focused on it for Audyr 🔥
With CoreSight, we rely heavily on user feedback to understand how people interact with the analysis outputs, and the hardest part is always separating the recurring themes from the one-off requests. How does Audyr handle feedback that's phrased completely differently but is basically asking for the same thing underneath?
@andreitudor14 Appreciate your comment! Under the hood Audyr uses AI to extract patterns and essentially identify if multiple feedback relate to the same problem. If that is the case it is simply merged into one grouped action. Would you say that Audyr could potentially help you with solving that issue at CoreSight? If you have any further questions on how Audyr works just let me know!
The claim that you tell teams what to build next is interesting. But what happens when the loudest users want something different from the silent majority — how do you weigh that?
@klara_minarikova Great question.
We don’t just weight input based on volume or who’s the loudest. Audyr uses the app’s context and knowledge base to ground feedback in what actually matters, like your product goals, target users, and the direction set by the founder.
So instead of “most requested = highest priority,” it’s more like which signals actually align with where the product is trying to go and are validated across different user segments.
That helps surface decisions that are not just popular, but actually meaningful for the product long term.
Curious how the conversational widget works in practice. Is it proactively prompting users mid-session based on behavior triggers, or is it more of a passive "leave feedback here" button? The difference matters a lot for response rates — passive widgets get ignored, triggered ones get gamed.
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most feedback tools don't fail at collection, they fail at synthesis. you end up with a tagged backlog of requests and someone still has to sit down and figure out what any of it means for the roadmap. ai clustering helps but it usually stops at "here are your themes," which is still a judgment call away from an actual decision.
what stands out here is the conversational widget approach, where the ai is probing in context rather than just logging a freetext box. combined with cross-source deduplication across channels like intercom, by the time something surfaces it's already been validated across multiple touchpoints, not just the loudest customer's voice. that's the part that's usually still manual. :)
@gabrielpineda Exactly, that’s been a consistent pattern we’ve heard from founders too. It’s rarely about collecting feedback anymore, it’s about turning it into something actionable without spending hours manually stitching context together.
That’s also why we focused so much on the in-context approach. The goal is that by the time something shows up, it’s not just a theme, but already carries enough signal and validation to support an actual roadmap decision.
Really appreciate you calling that out! You articulated the problem better than most 🙂