How do you draft a PRD from the requests that asked for it?
A PRD drafting AI Employee that clusters your inbound feature requests by theme, writes a first draft product spec for each theme, and cites the exact requests that motivated it. Then it hands the draft to a named product manager to finalize.
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Feature requests arrive faster than anyone can synthesize them. They land in the issue tracker as one off tickets, drop into Slack as a customer quote someone pasted, and pile up under labels that stopped meaning anything three quarters ago. By the time a product manager sits down to write a spec, the signal is buried under a hundred near duplicates, and the hardest part isn't writing the PRD. It's reconstructing which requests actually asked for this, and how many, and who. So the synthesis gets skipped or done from memory. A PRD gets written from the three requests the PM happened to remember, not the forty that were filed, and the spec ships without a paper trail back to the demand that justified it. When someone later asks why this feature and not another, there's no clustered evidence to point to, just a hunch that felt strong at the time.
The PRD drafting AI Employee reads open feature requests from the issue tracker, groups them into themes by what they're actually asking for, and writes a first draft PRD for each theme, covering the problem statement, the user need, and proposed scope, with every claim tied back to the specific requests that motivated it. It writes drafts to Docs and never publishes them as decided. It drafts and cites; a named product manager reads, edits, and decides what becomes real.
See exactly how the work gets done.
Runs on a weekly schedule
A scheduled run fires once a week. Each run starts clean and reads the current state of the request queue, so a draft always reflects what's actually open now, not a snapshot someone cached.
Clusters requests before it writes anything
What makes two requests the same theme, and how many requests make a theme worth drafting, travel with the AI Employee as a skill. Nothing is drafted until the queue is grouped and each cluster clears that bar.
Connects to your systems, with permissions you set
It reads feature requests from the issue tracker, pulls linked context from Slack threads, and writes one draft PRD per theme to Docs. Credentials stay in memory, never written to disk, never exposed to the model.
Cites the requests behind every draft
Each PRD names the requests that motivated it and links back to them in the tracker. The evidence for the spec is attached to the spec, so the PM edits from sourced demand rather than a summary they have to trust.
Hands off to the PM as a draft
It posts to the product channel with each new draft and its cited requests. A named product manager reads, edits the scope, and is the one who decides which drafts become committed PRDs.
Runs in your environment
Every run is isolated inside your own infrastructure. The request data never leaves it. Only the draft PRDs and the flag to the product channel do.
Draft only, never a decision
Every PRD it writes is marked draft. It cannot mark a spec approved, committed, or scheduled. That state change is the PM's alone.
Read only on the request queue
It reads feature requests to cluster them; it never edits, closes, relabels, or reprioritizes a ticket. Its only writes are the draft documents.
Every claim is sourced
It doesn't assert demand it can't cite. If a theme isn't backed by linked requests, it isn't drafted. The PM never edits from an unsupported spec.
You own every rule
The clustering rules, the theme threshold, and its per system permissions are yours, versioned and changed on your terms, not buried in a vendor dashboard.
A pile of one off requests that used to get synthesized from memory now arrives in the product channel as clustered draft PRDs, each one sourced back to the demand that justified it. The AI Employee drafts and cites; your PM edits, decides, and commits.
Every week
The request queue is clustered and drafted the same week, not from memory a quarter later
1 draft per theme
One first draft PRD per theme, each citing the requests behind it
0 published specs
Every draft held for a named PM to edit and decide
Our free AI audit shows you where AI fits, what your security risks are, and gets your first AI employee working.
How do you turn scattered feedback into a ranked list of what to build?
Gathers feedback from the help desk, public reviews, and Slack on a schedule, clusters it into themes with quotes and counts, and creates or updates one issue tracker issue per theme. It quantifies; people own what to build.
×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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