Product discovery for founders shipping with coding agents

For a technical founder, code is no longer the constraint. Claude Code or Cursor can scaffold a feature in an afternoon. The expensive mistake is shipping the wrong feature fast: a polished thing nobody asked for, built confidently on a half-remembered Slack thread.

Product discovery is how you close that gap. But the usual advice (run interviews, take notes, synthesize) collapses the moment you are also the engineer, the founder, and the support inbox. Notes pile up. Patterns live in your head. The agent gets a vague prompt and fills the gaps with plausible guesses.

Klyr is built for that exact spot. Upload the interviews and notes you already have, and get back themes where every claim links to a verified source quote. You decide what to build from evidence, not from the loudest conversation you happen to remember.

Drop in transcripts, call notes, support tickets, or sales emails. Klyr clusters them into themes, and every theme carries its citations: the exact quotes that back it. Themes with under two citations auto-drop, so a single vocal user does not masquerade as a trend.

From there, themes rank into a Feature Bet: the highest-evidence thing worth building next, with the demand and the quotes attached. No spreadsheet of guesses, no prioritization framework you abandon after a week.

A Feature Bet becomes a Build Pack: a PRD, acceptance criteria, and a coding-agent prompt written for Cursor or Claude Code. You copy it into your agent and it builds against real requirements, with the customer evidence baked into the spec instead of trapped in your head.

This is the part founders feel immediately. The handoff from "I think users want this" to "here is the spec, build it" usually lives in your working memory at 1am. Klyr makes it a copyable artifact. The agent stops guessing at scope because the scope is written down, with the quotes that justify each acceptance criterion.

See a full run end to end on the proof page: interviews in, cited themes, a ranked bet, and the Build Pack that comes out.

Every Cursor or Claude Code session starts cold. The agent has no idea why you shipped onboarding the way you did, which bet failed last month, or what a customer actually said in March. You re-explain context constantly, and the reasoning behind your product decisions evaporates between sessions.

Klyr keeps a persistent evidence-to-outcome graph: Product Memory. When you ship a Build Pack, the outcome learns back into it. Six months later you can ask why a decision was made and get the cited answer, not a guess. That is the moat: not the synthesis (anyone can cite a quote now) but the loop and the graph that remembers across every session your agent forgets.

An agent-context API and a Klyr MCP server let your coding agent read that memory directly, so the context you built once is available to the tool that keeps losing it.

Discovery that ends in a PDF dies in a PDF. Klyr is also a working PM surface so the decision does not leave the place the evidence lives.

You get tasks, boards, and sprints; PRDs and logged decisions; a Decision Room for review and a Spec Quality Gate before a bet becomes a build; automations and Klyr Agent Mode; notifications and imports. For a solo founder that means one place from customer quote to shipped feature to learned outcome, instead of five tabs and a memory that leaks.

Pricing is flat and self-serve from $0. No demo gate, no sales call to see whether it fits how you already work. Upload the notes you have, watch the loop run, and copy a Build Pack into the agent you are already using.

If cited synthesis is all you need, a free tool will do it. If you want the part that compounds (a Feature Bet you can defend, a Build Pack your agent can build, and a memory that survives the next session) start with the proof page or pricing.