How do you turn scattered feedback into a ranked list of what to build?
A feedback AI Employee that gathers input from your help desk, public reviews, and a Slack channel on a schedule, clusters it into themes with representative quotes and counts, and keeps one issue in your tracker per theme up to date, reading the feedback and writing the issues, while people still own prioritization.
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Product feedback arrives everywhere: support threads in the help desk, public reviews on G2 and the app stores, and an internal Slack channel where people drop what they hear. The same underlying request appears in all of them, worded differently each time, so it never gets counted. A theme that a hundred people asked for looks the same as a one off, because nothing pulls the mentions together. The common approaches don't add up to a roadmap. Reading each source by hand is slow and inconsistent, and whoever reads it weighs it differently. A tag in the support tool captures support but not reviews or Slack. A spreadsheet of feature requests goes stale the moment someone stops maintaining it, and it still doesn't tell you how many people asked for the same thing.
The feedback AI Employee runs on a schedule inside your own environment, with read access to the help desk, the public review sources, and the Slack feedback channel, and write access to the issue tracker. It gathers the feedback, clusters it into themes with representative quotes and counts, and creates or updates one issue per theme, so the same request is deduplicated and quantified instead of scattered. It reads the feedback and writes the issues; people still own prioritization.
See exactly how the work gets done.
Runs on a schedule
A scheduled run fires the AI Employee. Each run starts clean. It pulls the current feedback, reconciles it against the existing themes in the issue tracker, and updates them. Nothing carries over between runs except what's written to the tracker.
Works to your clustering rules
How to group feedback travels with the AI Employee as a skill: what makes two differently worded requests the same theme, how to pick a representative quote, how to title an issue, and how to match new feedback to an existing theme instead of creating a duplicate. As themes evolve, update the skill and the clustering stays consistent.
Connects to your systems, sources read only
It reads recent threads from the help desk, public reviews from G2 and the app stores, and the internal Slack feedback channel, and writes to the issue tracker, creating a new issue for a new theme or updating the count and quotes on an existing one. Credentials stay in memory, never written to disk, never exposed to the model.
Quantifies, never prioritizes
It is read only on every source, and its only write is to the issue tracker. It does not set priority, assign owners, or close issues. Those stay with people.
Keeps one issue per theme
Each run gathers the latest feedback, clusters it, and keeps one issue per theme current: representative quotes, a running count, and the sources it came from. The same request stops being scattered across three systems and becomes one quantified issue the team can weigh against the rest.
Runs in your environment
Every run is isolated inside your own infrastructure. It can reach only the sources it's scoped to, and only the issue tracker writes leave it.
Read only sources, scoped writes
The feedback sources are read only; the only write is creating and updating issues in the tracker. It does not prioritize, assign, or close.
People own prioritization
It quantifies a theme with quotes and a count. Whether a theme becomes roadmap, including its priority, its owner, and its close, stays with people.
Credentials stay contained
The help desk, review source, Slack, and issue tracker credentials connect with the permissions you set, stay in memory, are never written to disk, and are never exposed to the model or the logs.
You own every rule
The clustering rules, the skills, and the per system permissions are yours, versioned and changed on your terms, not in a vendor dashboard.
Feedback that used to sit unread across the help desk, reviews, and Slack now arrives as a set of quantified themes, each a single issue with quotes and a count. The AI Employee does the gathering and counting; the team still owns which themes become roadmap.
Every run
Feedback gathered, clustered, and reconciled to the issue tracker
One per theme
The same request deduplicated instead of scattered
4 sources
Help desk, reviews, and Slack into one issue tracker
Our free AI audit shows you where AI fits, what your security risks are, and gets your first AI employee working.
How do you draft a PRD from the requests that asked for it?
Clusters inbound feature requests by theme and writes a first draft PRD per theme, each one citing the requests behind it. The PM finalizes; it never ships a spec.
×How do you write release notes customers actually read?
Turns shipped work into customer facing, benefit framed release notes on each release, distinct from the internal changelog. PMM publishes, it never does.
×How do you tag every feature request to a roadmap theme?
Tags each inbound feature request to a roadmap theme as it arrives and routes it to the owner. It tags and routes only, and never reprioritizes the roadmap.
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