01
Listen
Bring interviews, support tickets, analytics, and team knowledge into one view. Separate evidence from assumption.
The AI-supported product decision system
Research DevelopmentLife Cycle
Get research, design, product, and leadership to one evidence-backed decision before engineering commits.
One decision system for the whole product team
Product designers
Product managers
Researchers
Founders
A product decision system your team can actually run.
Start with one prompt, keep one working record, and move only when the responsible people agree. No new account or heavyweight ceremony.
Explore the productWhat your team gets
One working record. Seven stages. A decision at every gate.
Each stage turns the team's evidence and judgment into a concrete artifact, then carries it forward without losing the decisions behind it.
Research Development Life Cycle
One shared record from signal to shipped learning
01Signals
Find the problems hiding in the noise.
The system brings scattered customer evidence together and surfaces the few problems worth investigating.
Stage output
Opportunity brief
Ranked opportunity briefs with source links and an explicit advance-or-stop decision.
What the working record holds
- Inputs
- Interviews, support tickets, analytics, sales notes, and team knowledge
- Work
- Cluster pain and workarounds by workflow, not feature request
- Evidence
- Every claim stays linked to its source or is marked as an assumption
- Decision
- Choose which signal, if any, advances to discovery
Fresh, source-linked evidence is required before discovery begins
How the product works
From scattered evidence to validated intent.
Listen to what people need. Define the decision. Prototype the riskiest path. Validate before engineering commits.
02
Define
Name the user problem, the business value, the risk, and what would prove the idea wrong before choosing a solution.
03
Prototype
Design the failure path first, make the smallest useful prototype, and review it together against UX laws and product principles.
04
Validate
Watch real people use it, measure comprehension and control, and hand off a validated intent that engineering can act on.
How your team contributes
Everyone can move the work forward.
Choose your role to see where your perspective changes the decision.
Listen
Turn evidence into a clear user journey and surface the gaps that still need attention.
Define
Write the mental model and make failure states visible before the interface takes shape.
Prototype
Build the smallest experience that answers the riskiest open question.
Validate
Revise the interaction with users and bring the learning back to the team.
The AI keeps the shared record. People bring the evidence, judgment, constraints, and decisions.
Made to carry into delivery
Give engineering more than a ticket.
The working record travels with the product, so scope and implementation stay connected to the evidence and decisions behind them.
- The evidence behind the scope
- The risks and designed failure paths
- The non-goals and open questions
- The validation decision and next step
Use the product now
Start the decision in the AI app you already use.
Choose a starting point, then paste the generated prompt into ChatGPT, Claude, Gemini, or another AI app.
The builder runs in your browser. This website does not send or store anything.
Prompt builder
No account required
What are you trying to do?
The second option includes a compact, self-contained version for AI apps that cannot open web links. Read Conversation Mode.
Give the team a decision they can build from.
Start with one prompt. Leave with shared intent, visible evidence, and a validated next step.
Start with your AI appBuilding inside a repository? Add the workspace version.
The workspace version keeps persistent, versioned decisions with the product so teams can resume work, hand off context, and run deeper research and validation.
curl -fsSL https://rdlc.org/install.sh | sh