Know what to build before AI writes the code.
Klyr turns customer interviews, feedback, and usage data into ranked product bets, cited PRDs, and builder-ready tasks, with the evidence attached at every step.
Implementation is getting faster. Product clarity is becoming the bottleneck.
Speed only helps when the team knows which customer problem is worth solving and why, so Klyr keeps product discovery, decisions, work, and outcomes in one chain that survives the handoff to delivery.
Evidence is the source of truth
Every recommendation, PRD, task, report, and decision keeps a visible trail back to customer signals and product context.
One loop instead of scattered artifacts
Klyr connects discovery, planning, delivery, reporting, and outcomes so the team stops rebuilding the story in separate documents.
AI proposes, people decide
Plans, tickets, contradiction checks, and reports stay review-first so the product record changes only when the team accepts it.
Product Memory compounds
Every shipped bet, missed assumption, customer proof point, and outcome review becomes reusable context for the next decision.
From raw evidence to implementation-ready product direction.
Upload evidence
Bring in interviews, feedback, sales notes, support themes, and product usage context before opinion fills the room.
Ask what to build
Ask the question every team is really debating and get ranked product bets grounded in the source material.
Generate the plan
Turn the strongest bet into a cited PRD, risks, acceptance criteria, and the decisions the team needs to make.
Hand off to builders
Create implementation tasks with UI, data, workflow, and evidence context ready for the people or agents doing the work.
Learn from outcomes
Record what shipped, what changed, what worked, and what Klyr should remember before the next product bet.
Built for founders and teams who need the same clear product truth.
Clear priorities without chasing status.
See which product bets are backed by evidence, where the risks are, and what the team learned after shipping.
Portfolio view across projects with status, owner, blockers, and risk score
Outcome reviews showing what shipped, what missed, and what should change next
Status updates backed by task, decision, and research history
Decision log with the rationale and evidence behind every major product bet
The output feels useful because the evidence never leaves it.
Klyr pairs customer interview synthesis with a living research repository, connecting signals, product bets, PRDs, tasks, decisions, reports, and outcomes so every recommendation has a trail back to what customers said.
One theme from a real synthesis.
Every theme arrives ranked, scored, and pinned to the moments customers actually said it.
Users abandon onboarding at the workspace setup step
“I just wanted to try it. Why are you asking me to invite my whole team?”
“I closed the tab when it asked for my company size.”
The evidence does not stop at the report. It carries through the decision and into the build.
A theme becomes a ranked bet, the bet passes a quality gate, the call is made on the record, and the same evidence graph is handed to the coding agent that writes it. The trail from a customer quote to a shipped line of code stays unbroken.
Feature Bets, ranked by evidence
Each bet carries its problem, target users, confidence, the supporting themes, and the counter-evidence that argues against it, so the team bets with its eyes open.
Spec Quality Gate
Before a spec reaches a builder, Klyr scores it for evidence depth, testable acceptance criteria, and prompt clarity. Weak specs stay in draft; ready ones go to handoff.
Decision Room
Sign-off happens on the record, next to the evidence: founder, PM, design, and engineering approve, reject, or send a bet back, so the why-did-we-build-this question always has an answer.
Evidence graph and coding-agent context
Every signal, bet, decision, and outcome stays linked in one graph. Hand that context to Claude Code or Cursor over MCP so the agent builds against the evidence, not a guess.
Start with one evidence pass, then scale the product memory with the team.
Launch pricing scales by evidence volume, collaboration, and the amount of PM workflow you want connected around product decisions.
Compare all plansEvaluate Klyr with sample work and a small real research pass.
A focused PM workspace for ongoing discovery, planning, and reporting.
More synthesis capacity and PM operating-system depth for active product work.
Shared evidence, decisions, and delivery memory for a small product team.
The feature set stays evidence-first on every plan.
Evidence PM OS
- Research synthesis with citations
- Evidence-linked tasks, PRDs, goals, and decisions
- Product Memory across recommendations and outcomes
Execution workspace
- Board and list task views
- Sprint planning and workload summaries
- Docs database for PRDs, decisions, reports, and notes
Decision and handoff
- Feature Bets ranked by evidence, confidence, and counter-evidence
- Spec Quality Gate scores a spec before it reaches a builder
- Decision Room keeps sign-off on the record and linked to evidence
AI controls
- AI proposes changes; nothing applies until you accept
- Klyr Agent Mode for task and project work
- Automation previews and run logs
Team readiness
- Workspace roles and invites
- Global search across projects, docs, tasks, and evidence
- Integration-ready handoff payloads
Product Memory compounds with every decision the team makes.
Every cycle leaves a record behind: the recurring pain customers keep naming, the ideas that failed, the bets that proved out, the tradeoffs the team already weighed.
That record accumulates into company-specific context the next decision can use, so each product bet starts from everything the team has learned, not a blank page.
Output you can hand to the team, not output you have to defend.
Upload evidence and ask what deserves to exist.
Start with a transcript, sales note, support theme, or sample project and turn raw signal into a recommendation the team can inspect.