Turn a backlog of feedback into decisions you can defend
Support tickets pile up in Zendesk, NPS verbatims sit in a spreadsheet, and interview notes scatter across Notion. Idam AI's Feedback Analysis agent reads all of it, clusters it into ranked themes with the verbatim quotes still attached, and hands you roadmap recommendations - not another summary to re-summarize.
traceable to a real quote
free trial
Synthesis that holds up when a stakeholder asks “says who?”
Anyone can ask a chatbot to summarize feedback. The hard part is synthesis you can take into a roadmap review: themes counted across sources, quotes preserved verbatim, and contradictions surfaced instead of smoothed over.
Ranked by frequency and impact
Themes land in a priority matrix, not an unordered list. A complaint mentioned once by a churned enterprise account is weighted differently than a cosmetic nitpick mentioned ten times.
Every insight keeps its receipts
Verbatim quotes travel with each theme. When engineering asks whether “users are frustrated” is real or vibes, you show the exact words, who said them, and how often.
Ends in recommendations, not a recap
The output is a set of roadmap recommendations tied to themes - and each one can flow straight into a user story, PRD, or one-pager in the same workspace.
Your feedback lives in six places. Your analysis shouldn't.
Paste raw notes, upload transcripts and spreadsheets, or pull tickets and analytics from connected tools. The agent merges every source into one pass - so the Tuesday support spike and last month's interviews finally get read together.
Bring feedback from wherever it hides
Interviews, surveys, usability tests, support queues, sales call notes, app-store reviews. Mix formats freely - the agent normalizes everything before it starts looking for patterns.
Contradictions surfaced, not averaged
When power users and new users disagree, the agent says so and shows both sides - instead of blending two real signals into one misleading middle.
Triangulated across sources
A theme that shows up in tickets, interviews, and NPS carries more weight than one that lives in a single channel. Cross-source frequency is counted for you.
Grounded in what users actually said
The agent works from your data, not its imagination. If the evidence for a theme is thin, it tells you the sample is small rather than inventing confidence.
hundreds of raw feedback items, distilled into a handful of decision-ready themes in a single session
Before you paste in 200 tickets
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