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.
Forum Threads
View all
this looks really cool! does this replace my jira/asana board?
ProductBridge
@amelia_wampler Absolutely. We have already seen organizations moving towards a single platform for customer feedback collection + product mangement.
You can just connect all your sources - like Intercom, Slack, Zendesk, Gong, Trustpilot, Playstore/Appstore and AI automatically monitors these channels for the feedback - so that you don't have to spend time on this scattered feedback. Once you have insights, you can take clear roadmap decisions.
Give it a try!
The cross-platform aggregation problem is harder than it looks on the taxonomy side. When you're pulling feedback from, say, Reddit vs. G2 vs. in-app surveys, the same underlying complaint gets expressed in completely different registers. If you're using any kind of category classifier to group feedback, watch for the taxonomy itself creating false signal — I ran into a case where a hand-maintained category list was so specific to one domain's vocabulary that topically identical ideas from a different source never clustered together. The fix isn't better keywords, it's letting the classifier infer categories from the actual content distribution rather than pre-labeling buckets.
I like the direction of the product. As a PM, I always use Dovetail to aggregate user reviews from different channels. But the "feature brainstorming" and "prioritization" part I do with my co-pilot, but it's a second surface (outside Dovetail) so I have to switch between them. Kudos for solving these problems as I see it and both important parts can be done with the same tool.
ProductBridge
@tanyavrpvThank you! This is exactly the workflow we set out to fix.
That's exactly what we wanted to fix. With ProductBridge, the feedback aggregation, AI-powered insights, prioritization, roadmap, and changelog all live in one place. No jumping between surfaces, no losing context mid-decision.
As a PM you'll feel the difference immediately — would love for you to give it a try! 🙌
ProductBridge
Hi@tanyavrpv, A fully upgraded version for collecting feedback from different sources as well as prioritization feature is live.
Once you connect the channels like Intercom, Zendesk, Trustpilot, Appstore, Playstore etc, AI automatically monitors these channels and pulls the feedback if there is any to feedback boards. From there, you can prioritize and assign the feedback items to the team, create roadmaps, and automatically communicate with the user who raised this feedback. Once shipped, users will be auto-notified about their feedback.
Please give it a try!
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.
NOVA
Feedback is everywhere — support tickets, Slack, emails, user calls, but turning it into clear, actionable insights is still a big challenge for most teams.
ProductBridge seems to be tackling exactly that gap. If done well, this could really help teams prioritize better and build what users actually need.
Curious, how does ProductBridge handle deduplicating and prioritizing feedback across different sources?
Congrats on the launch and excited to see how this evolves 🚀
ProductBridge
Thank you, @dharmikp1908! 🙌 You've described the problem perfectly.
On dedup: we use advanced RAG + LLM, so matching happens at the intent level, not keywords. The AI already knows your full context — knowledgebase, existing feedback boards, roadmap, and changelog. So the same problem coming in from Slack, a support ticket, and an email gets grouped correctly, even if the wording is completely different.
On prioritization: it's not just vote counts. Users can be tagged with properties like MRR and revenue tier, so you're always seeing who is asking, not just how many.
Really excited to build this out further — appreciate the support! 🚀
Zennbox
Feedbacks are like goldmine, just curious how will ProductBridge will filter out feedbacks from tons of other contents. Congratulations on the launch. This looks a great product that will genuinely help businesses.
ProductBridge
@bhu_1 Love that analogy — goldmine is exactly right. The problem is most teams are digging with their bare hands! :)
ProductBridge uses AI to separate real feedback from noise at every step. When feedback comes in from any channel — Slack, support tickets, emails — the AI classifies it automatically. Duplicates get grouped.
What's left is clean, structured, actionable signal. No manual sorting needed. 🙌 Thank you for the kind words!
Bringing feedback from multiple channels into one place and actually turning it into roadmap decisions sounds really useful. I like the focus on closing the loop with users after features ship. How does ProductBridge detect and merge duplicate feedback across different sources without losing important context?
ProductBridge
@vik_sh Thank you! Great question — and the "without losing context" part is key.
We use RAG + LLM to match feedback by intent, not keywords. So the same problem described differently across Slack, support tickets, and email still gets grouped correctly.
You see the full picture: how many people raised it, what they said, which channel it came from. Nothing gets lost, just organized.
If the feedback agent is not enough confident on the grouping, it will flag it for the review. 🙌