The system of record for what to build and why.
Turn customer interviews, sales notes, product decisions, PRDs, tasks, and outcomes into one evidence-backed operating system.
From raw signal to shipped learning, without losing the thread.
Capture evidence
Collect interviews, sales notes, feedback, metrics, and research context before work turns into opinion.
Synthesize decisions
Turn signals into recommendations, opportunity plans, PRDs, and decisions with citations attached.
Plan work
Create tasks, owners, acceptance criteria, and handoff context from the same evidence trail.
Track outcomes
Review what shipped, what missed, what changed, and what the team should remember next time.
Built for the people who decide, design, sell, and ship the product.
Evidence-backed priorities, without chasing status.
Get outcome reports, risk signals, and progress visibility that trace back to customer evidence — not filtered updates from whoever is loudest in the room.
Portfolio view across all projects with status, owner, blockers, and risk score
Outcome reviews that show what shipped, what missed, and what the team should carry forward
AI-generated status updates backed by actual task and decision history
Decision log with the rationale and evidence behind every major product bet
The AI layer is useful because it remembers the evidence.
Klyr connects research, roadmap bets, execution tasks, decisions, and outcomes so AI assistance stays grounded in the team's actual product history.
Natural-language task and sprint planning that proposes operations for your review — Cmd+K anywhere in the workspace.
Every task traces back to a research quote, theme, PRD, or customer note so execution never loses the why.
A company-specific record of recurring pain, failed ideas, proven wins, and prior tradeoffs that gets more useful every session.
Generated from research, editable after saving, and stored with the citations and evidence that support them.
Close the loop by recording what shipped, what missed, metric movement, and what to carry forward.
Read-only project snapshots for stakeholders and structured export payloads for Linear, Jira, and GitHub.
Structured task and PRD exports for your existing engineering workflow with evidence context attached.
Plausible-sounding wrong answers destroy trust faster than slow ones.
Every recommendation should trace to customer evidence, product reasoning, implementation work, and the outcome that followed.
That history becomes Product Memory: a company-specific record of recurring pain, failed ideas, proven wins, and prior tradeoffs.
Output you can hand to the team, not output you have to defend.
Start with your next research session.
Upload a transcript, run a synthesis, and see your first evidence-backed recommendation in under two minutes.