Name a product, or paste your own feedback. Cherry bites through the noise — ranks what stings, routes who owns it, and tells you what to do.
Reviews, forums, Reddit, social, news — Cherry searches the open internet and keeps the source link for every issue.
It names each theme and groups the hundred ways people describe one problem into a single issue.
Severity and prevalence combine into one score, so the top of the list is genuinely the top.
Disagree with a call? Tell Cherry, and it re-ranks with your judgment — sharper every pass.
Under the hood: the same journey in eight stages, ending where it starts.
Cherry is the synthesis layer — it pulls signal from where customers already speak, and pushes work to where teams already act. It connects to systems of record; it doesn't try to replace them.
● Live today: Slack routing. The rest is the integration design — each intake source is a thin adapter into the same triage core, and issue status flows back from the linked work item, so there's no manual documentation tax.
The honest part · v1 → v2 Where Cherry breaks at scale — and what v2 does about it → Ten break→fix cards: who wins when two reviewers disagree, cost guards that survive a cold start, staged prompt rollout, contract-driven data permissions, and the operability floor. A demo that knows where it breaks is halfway to a platform.Feedback systems obsess over intake, but the question that decides whether one is useful sits on the exit side: who reads the mail, and what does each reader need? Cherry's answer, drawn — five sources with their biases, three jobs in the middle, five different cuts on the way out, and the return arrows that make it a loop instead of a funnel.
Internal tools have product-market fit when teams can't make a decision without them. Cherry's bet is that feedback earns that seat by being cut per reader — "high-signal" is a property of the audience. Read the full essay →
Cherry drafts every ticket and never sends one on its own. That's the design: the model does the repetitive first pass, a person makes the calls that need context the model was never given, and the system measures whether each correction genuinely helped — so tomorrow starts ahead of today.
The edit is a data point; the measurement is what turns it into learning. Read: why the human stays in the loop →