A project-management SaaS had feature requests scattered across support tickets, sales calls and community threads — over 500 of them, uncounted. We built a pipeline that captures every request, clusters them by AI into 42 canonical themes, and ranks each by revenue at stake.
Typical results
42
Canonical themes
30 min
Monthly triage
5→1
Inboxes to check
Figures reflect typical results for this kind of automation.
The challenge
Feature requests arrived through five doors — support tickets, sales-call notes, community threads, NPS comments and a request form — and lived in five different tools. The same feature appeared under a dozen phrasings, nobody could say how often anything was requested or by whom, and roadmap debates ran on anecdotes. Deals were lost over features that had been requested forty times.
Before and after
Monthly triage effort
−94%Tools to check
−80%Phrasings per feature
−92%The automation
We connected every source to one pipeline: requests are extracted automatically from tickets, call transcripts, community threads and forms, each carrying the customer, their plan and contract value. OpenAI matches each request to a canonical feature — merging phrasings, creating new themes only when nothing fits — and the result is one live board where every theme shows request count, revenue at stake and representative quotes, re-ranked as new requests land.
How it works
- 01
Capture from every channel
Support tickets, sales-call transcripts, community threads and form submissions are scanned for feature requests automatically.
- 02
Enrich with revenue context
Each request is linked to the requesting customer, their plan and contract value from the CRM.
- 03
AI matches to canonical themes
OpenAI merges phrasings into canonical features, creating a new theme only when nothing existing fits.
- 04
One ranked board
Every theme shows request count, revenue at stake and real quotes — re-ranked as new requests arrive.
- 05
Close the loop
When a feature ships, everyone who asked for it is flagged for a personal heads-up from their account owner.
Starts from
- Support tickets
- Sales calls & community
Delivers
- Ranked feature board
- Revenue-at-stake scores
- Trend alerts to product
The impact
More than 500 scattered requests collapsed into 42 clean, ranked themes on the first run. Product planning now starts from revenue-weighted evidence instead of the loudest voice, monthly triage shrank from a full day to a 30-minute review, and sales can tell customers their request was genuinely logged.
Questions about this automation
Where are feature requests collected from?
The pipeline scans support tickets, sales-call transcripts, community threads, NPS comments and the request form. AI picks out the sentences that describe a feature request and ignores the rest of the conversation. Each request is linked to the customer who made it, with their plan and contract value pulled from the CRM.
How does the AI avoid creating duplicate themes?
OpenAI compares each new request with the current list of canonical features and attaches it to the closest match. A new theme is created only when nothing on the list fits. Product managers can merge or split themes on the board, and the updated list is what future requests are matched against.
How do customers hear back when a requested feature ships?
When a theme is marked as shipped, the flow lists every customer who asked for it and creates a task for each account owner to send a personal heads-up, quoting the customer's own words from the original request. It is a multi-step build, usually rolled out in stages, with support tickets as the first source.