Feature requests arrive from everywhere when you're building a product. Sales says a customer mentioned something on a call, support says a theme keeps coming up in tickets, and users message you directly. Requests end up scattered across Slack, email threads, and the corner of a spreadsheet.
Most teams get as far as pulling those requests into one place. They log them, count how often each one comes up, and work down the list starting with whatever has the most requests. It's a common approach. But plenty of teams later realize this alone doesn't actually tell them what to prioritize.
Counting doesn't tell you what to prioritize
Say "search is slow" comes up in 10 requests. Looking at the count alone, it looks like a clear priority. But read each one, and the 10 requests often turn out to be about different things.
One person means "I have to wait several seconds for results, and it's frustrating." Another means "some of my data isn't included in search results at all." Same headline, "search is slow," completely different problems. The first is about response time; the second is about what gets indexed. Both are worth fixing, but "10 requests came in" alone doesn't tell you which to fix first.
A count tells you how loud a request is. It doesn't tell you why people want it. That sounds obvious, but it's easy to lose sight of day to day. As requests pile up, there's less and less time to read each one and infer the reasoning behind it, so teams end up prioritizing by volume and loudness by default.
It gets harder still because getting to the "why" usually means going back to whoever made the request and asking. That's especially true when a request arrived secondhand through sales or support. More often than not, that follow-up never happens, and the count just keeps climbing.
Putting the reason into words
That's the problem we built Feedury to solve. In terms of posting requests, reacting with empathy (voting), and tracking status, it's in the same category as other feedback management tools (we cover how to pick one in "What Is a Feedback Management Tool? A Guide for Indie Developers and Small SaaS Teams").
Where Feedury differs is that summarizing who's affected, what's frustrating them, and why they want it is built into the collection flow itself, not bolted on afterward. After someone submits a post, we can optionally show a short follow-up question. For a "search is slow" post, that might be "what were you doing when this happened?" with a few quick-pick options. Answering appends the response to the post. It's entirely optional, so it doesn't slow down anyone who'd rather not answer.
Similar requests get grouped automatically, and you can see the combined empathy across the group. The goal is to stop a request with a small number of votes but a serious underlying reason from getting buried under a louder, larger one.
Decisions like merging requests or moving a status forward are always made by a person. AI is useful for surfacing patterns and drafting summaries, but letting it decide what counts as "the same request" or "worth building" felt like too much risk of drifting from what the team actually knows.
Being upfront
Feedury just launched, and we don't have customer stories to share yet; we're looking for our first users right now. We think the underlying problem here, request counts drifting from real priority, shows up everywhere, regardless of industry or where a team is based.
Every feature is free to use right now, and you don't need a credit card. If this sounds familiar, take a look at the demo board: a public board built around a fictional task management SaaS, so you can see real posts, statuses, and votes in action.
Most teams already have some way of collecting requests. Adding the extra step of capturing "why" changes how you prioritize more than you'd expect. Hope this was useful if you've been wrestling with the same problem.