What it is.
It scrapes support systems, user forums, social platforms, and review sites through Bright Data, then uses a model to categorize and prioritize what it finds by frequency and user impact.
Results go to Google Sheets and into Jira as tickets, so feedback turns into work somebody owns. The consolidation matters more than the automation: most teams have this data and no single view of it.
What you get.
- Feature requests collected from support, forums, social, and review platforms.
- AI categorization and prioritization by frequency and user impact.
- A Google Sheets record of every request found.
- Jira tickets created from the prioritized requests.
How to set it up.
List the channels where your feedback arrives, then configure only those.
Add Bright Data credentials, since the scraping steps depend on them.
Check the categorization on a sample by hand, because a miscategorized request disappears into the wrong bucket.
Set the Jira project and issue type, and add a label so imported requests stay identifiable.
Set a frequency floor before anything reaches Jira, or a single loud customer creates a roadmap.
Review the sheet monthly, since the ranking is more useful than any individual ticket.
Use cases
Collect feedback from every channel
Pull requests out of every channel into one queue.
Categorize before the triage meeting
Let it group and rank so the meeting starts from a sorted list.
Land it where product works
Create the ticket so the request does not die in a spreadsheet.
Best for
Feedback scattered across channels
Support, a forum, and two review sites all hold requests nobody has counted together.
Roadmap arguments
Frequency across channels is a better input than whoever emailed the CEO last.
Review site monitoring
Feature complaints on review sites are public and usually unread internally.