Launching today
Your feedback is everywhere — Slack threads, Intercom support tickets, review sites, DMs. ProductBridge's AI agent collects it all automatically, organizes it, deduplicates, and helps your team ship what users actually want. Users request features, upvote, and watch ideas move through your public roadmap. Teams prioritize with data, publish changelogs, and auto-notify users when their feature ships. One platform. Complete feedback loop. Flat pricing. No seat fees. No surprises. Ever.
ProductBridge
Banyan AI Lite
Happy launch team! Quick question: How do you handle context and prioritization when aggregating feedback from so many different sources? For example, how do you distinguish between loud but low-impact requests and signals that actually represent broader customer demand, and how reliable is the deduplication when similar feedback is phrased differently across channels?
ProductBridge
Thanks for the kind words, and great questions,@davitausberlin
On prioritization: we don't just count votes. Every user in ProductBridge can be tagged with properties — like the MRR they bring in, their plan type, or any custom attribute. So when feedback comes in, you're not just seeing how many people asked — you're seeing the weight behind who asked. A request from 3 high-MRR customers can and should outrank 20 requests from free users.
On dedup across channels: we use advanced RAG + LLM, so matching happens at the intent level, not keyword level. And the AI already knows your full context — knowledge base, existing feedback, roadmap, and changelog. So the same problem phrased differently across Slack, Intercom, and email gets grouped correctly.
We collect client feedback across several channels at once — and deduplication is what interests me most. The same request often arrives three times, worded differently, and it's hard to tell if it's one problem or three. How does ProductBridge decide two pieces of feedback actually belong together?
ProductBridge
Great question @klara_minarikova — this is core to how ProductBridge works.
We use advanced RAG + LLM to match feedback by intent, not just wording. But the real differentiator is context — our AI already knows your full board. Knowledgebase, existing feedback posts, what's on your roadmap, what you've already shipped in the changelog.
So if someone requests something you launched 2 months ago, it knows. If 3 people describe the same problem differently, it groups them.
The "closing the loop" part is what I care about most here. We've tried a couple feedback tools before and the collection part is usually fine, but actually telling users "hey we shipped the thing you asked for" always falls through the cracks.
$24/mo flat is solid too. Most tools in this space charge per seat which gets painful fast when you want the whole team to have access.
How does the AI handle feedback that's more of a rant vs an actual feature request though? That's always been the tricky part for us.
ProductBridge
@mihir_kanzariya The loop-closing problem is exactly why we built the changelog + notifications the way we did — it's automatic. Ship a feature, every user who asked gets notified. Zero manual effort.
On rants: the AI reads the frustration and pulls out the real problem underneath. Actionable signal, not noise.
And yes — flat pricing, whole team, no surprises unlike most of the feedback management platforms out there.
TruGen AI
Congrats on the launch! @hareesh_vemasani @rohithreddy
Honestly, this is something most teams just deal with instead of solving.
Feedback keeps coming in, but it rarely turns into clear product decisions.
Really like how you’ve made it more structured and usable.
Curious, what kind of feedback patterns surprised you the most so far?
ProductBridge
Thank you so much! 🙌 @bhavyasree
Biggest surprise: teams discovering that the same problem had been reported 12+ times — just never connected. Different words, different channels, different teammates receiving it. Once it's all in one place, the priorities become obvious really fast.
really like this because feedback usually ends up scattered everywhere and teams lose a lot of time just trying to piece it together. the closed-loop part stood out to me since users rarely know what happened after sharing feedback. which source is giving you the most valuable insights so far: support tickets, reviews, or slack conversations?
Documentation.AI
Great work @hareesh_vemasani 👌really love how you’ve tackled such a real and messy problem. As someone working in growth and SEO, I’ve seen how scattered feedback across channels often leads to weak prioritization and missed insights. What stands out here is the full loop from collecting feedback to actually closing it with users through changelogs and notifications, that’s where real trust and retention are built. Also the flat pricing is a smart move in a space crowded with seat based models. Curious to see how it performs at scale, but this looks genuinely useful for product teams...