A customer interview synthesis tool that closes the loop

Synthesis is not summarization. A summary tells you the gist of one call. Synthesis tells you what twelve calls have in common, how strong each pattern is, and which quote you can put in front of a skeptical exec without getting caught paraphrasing.

Klyr ingests your interviews, notes, and support threads, then clusters them into themes. Every theme is backed by verified source quotes, and any theme that cannot clear two real citations is auto-dropped. That last rule matters more than it sounds: it kills the confident-sounding pattern that turns out to rest on one loud customer.

The output is not a wall of bullet points you still have to defend. It is a set of themes you can click into, each one showing the exact transcript lines it came from.

Upload your transcripts and notes. Klyr reads across the whole set and builds themes where each supporting point carries a citation back to the line it came from. When you hover a claim, you see the quote and which interview it belongs to, so review is checking sources rather than re-reading everything.

The two-citation floor is the part that keeps this honest. Research synthesis tends to drift toward whatever the writer already believed; requiring at least two verified quotes per surviving theme forces the evidence to do the work. You end up with fewer themes, but you can stand behind all of them.

You can see this run end to end on a real example at the proof page, no signup required.

Honest answer first: if all you need is to ask questions of a pile of transcripts and get answers with citations, NotebookLM does that well and it costs nothing. So do the major coding agents. Cited synthesis is table stakes now, and any page that pretends otherwise is selling you something.

The difference is the loop. NotebookLM gives you a great answer in a chat window and then forgets it. Klyr turns the synthesis into a ranked feature bet, then into a Build Pack: a PRD, acceptance criteria, and a prompt you can hand straight to Cursor or Claude Code. When the work ships, the outcome flows back into Product Memory, so the next round of interviews lands on top of everything you already learned and decided.

Your coding agent forgets every session. Klyr is the memory it loses. That persistent evidence-to-outcome graph, not the citations, is the reason to bring research into Klyr instead of a chat tool. See the full side by side at Klyr vs NotebookLM.

A cited theme is an insight. A roadmap needs a decision. Klyr ranks the themes into a feature bet so the question shifts from what did customers say to what should we build first and why.

From the chosen bet, Klyr generates a Build Pack: a PRD, acceptance criteria, and a coding-agent prompt scoped to the evidence behind the bet. The agent prompt is grounded in the same quotes that survived synthesis, so what gets built traces back to what customers actually said, not to a game of telephone through three documents.

Klyr is also a real PM workspace around this: tasks, boards, sprints, PRDs, decisions, and a Decision Room for review. The synthesis is the front door, not a detached report you export and lose.

Klyr fits PMs and researchers who run discovery continuously and are tired of synthesis dying in a slide deck. If you do interviews, decide what to build, ship it, and want each cycle to compound instead of starting from a blank page, this is built for you. Pricing is flat and self-serve and starts at $0, so you can run a real batch before deciding anything: see pricing.

It is the wrong tool if you only need to interrogate one document occasionally, or if your team has no intention of acting on the research. In those cases a free chat tool is genuinely enough, and we would rather you use one than overpay us.

The whole pipeline is documented at docs, and you can start a workspace from the dashboard.