Build from evidence, not the loudest voice.
Every theme ranked by the revenue behind it, so the sprint you pick is the one that moves the number.
Your team has 200 customer conversations a week and reads maybe fifteen. ClosedLoop AI reads every one, clusters the same pain across every tool, and ranks it by the revenue behind it.
SOC 2 Type II · ISO 27001:2022
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Ship what moves revenue
Every theme ranked by the ARR behind it, attributed from your CRM.
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Stop debating priorities
Every decision carries its quotes, accounts, and numbers.
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Catch churn before renewal
Dissatisfaction surfaces from calls and tickets weeks early.
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Answer "who asked for this?" instantly
Every feature traces back to the named customers who asked.
One read across calls, tickets, CRM, chat, and 40 more sources.
You don't have a feedback problem. You have an aggregation problem.
The evidence is already there. It is scattered across six tools, and nobody has read all of it in one place.
Calls, tickets, CRM notes, chat, surveys, and usage all hold the same pain in different words. No one has time to read every source, so the backlog drifts back to opinion and the loudest account on the last call wins the sprint. The pattern twenty customers described quietly, across four channels, never gets counted.
- The reading does not scale, so most of it is never read.
- The loudest voice wins over the quiet pattern.
- The tagging tax goes unpaid, and the repo goes stale.
- Nobody can answer "who asked for this?" on the spot.
What changes, and the mechanism behind it.
Four outcomes for the product team, each tied to the thing the agent actually does. No tagging, no taxonomy, no spreadsheet.
Ship what moves revenue
Every theme is ranked by the ARR behind it, attributed from your CRM. The sprint you pick is the one that moves the number, not the one the loudest account asked for on the last call.
Stop debating priorities
Every theme opens with its evidence attached: the verbatim quotes, the named accounts, and the revenue at stake. The room argues about the decision, not about whose anecdote is more real.
Catch churn before renewal
Dissatisfaction shows up in calls and tickets long before it shows up in a renewal forecast. ClosedLoop AI surfaces the drift while there is still time to act on it.
Answer "who asked for this?" instantly
Leadership asks why a feature is in the plan. Every theme traces back to the named customers who raised it, in their own words, ready before the question lands.
Fills with the named customer, title, and company from your data, once the quote is real and approved.
A research repo files feedback. ClosedLoop AI decides what to build.
Point tools organize what you already read. ClosedLoop AI reads everything and ranks it. Keep the repo, connect it as one more source.
| Capability | Manual and point tools | ClosedLoop AI |
|---|---|---|
| Reads every conversation across every tool | Not available | |
| Dedups the same pain across channels | ||
| Ranks by the revenue behind each theme | Not available | |
| Works with zero manual tagging | Not available | |
| Answers "who asked for this?" in one query | ||
| Closes the loop with customers when you ship |
Built in Manual or partial Not available
Proof gets measured, never manufactured.
Two revisions are open. Each lands here only when it clears the standard below: real, attributable, approved. An empty slot beats a manufactured one, so these stay open until then.
An attributed number from our own analyzed corpus. Real and recomputable, or nothing.
A verbatim customer quote with a real name, title, and company. Approved, or nothing.
What product teams ask first.
If yours isn't here, support@closedloop.sh is the fastest path. We answer within a business day.
How is this different from Productboard or Dovetail?
Those tools ask you to tag every quote, then group the tags by hand. ClosedLoop AI does both automatically and ships the artifact, a ranked weekly brief, to where you work. It also points at the one theme worth fixing first, weighted by the revenue behind it.
How does it dedup across channels?
A theme is a cluster of semantically related quotes across every source. The same customer saying export is broken on a call and in a ticket counts as one insight, not two. ARR is attributed once, at the account level, from your CRM.
Does it push to Linear and Jira?
Yes. Top themes draft as tickets with the quotes attached. When one ships, ClosedLoop AI surfaces every customer who asked so CS can close the loop, in the customer's own words.
Will it replace our research repo?
No. It reads what you already have, including Notion, Dovetail, and FullStory, and treats each as another source. It is a layer on top, not a migration.
What if a customer says contradictory things?
You see both quotes side by side, dated and attributed. ClosedLoop AI is built to keep the conflict visible rather than flatten it. You decide.