Who belongs here
Product leaders, product managers, designers, researchers and product operations teams working with engineering to discover, test and improve products.
Proposition
AlisX connects continuous discovery, AI prototyping and delivery around shared evidence. Hear from more customers, test assumptions with working prototypes and carry what you learn into the next release. Give product, design, engineering and their AI tools the context to make better decisions together.
The door
Alis Ideate Interviews, Alis Ideate Artifacts, Alis Build Workstations, Alis Ideate Projects, Skill Store, Alis Build DBDThe problem to go in with. AI makes prototypes quicker, but collecting useful feedback, testing assumptions and carrying decisions into delivery still breaks the learning loop.
The tools to go in with. Alis Ideate Interviews · Alis Ideate Artifacts · Alis Build Workstations · Alis Ideate Projects · Skill Store · Alis Build DBD
What they get. Connect customer discovery, working prototypes and feedback to shape your next useful release.
Ask first
- Which recent product decision waited on evidence or feedback?
- How did you test the last prototype, and how did the answers change the next iteration?
- What gets lost between research, requirements and the engineering handover?
Lead with
Run Alis Ideate Interviews around one prototype with its intended users, then turn the findings into a prioritised next iteration and explicit assumptions.
Backup: Bring the evidence and decisions for one product bet into an Alis Ideate Project and review its requirements with engineering.
Demonstrate or verify
- Trace a user observation through a decision to an updated artifact.
- Show the feedback loop on their prototype; do not treat interview answers as market validation without further evidence.
Further, Faster, Safer
Further: Test more ways to solve the customer problem. Explore alternative solutions with real customers and working prototypes, giving product, design and engineering more evidence before committing to a build.
Alis Ideate Interviews · Alis Ideate Artifacts · Alis Build Workstations
Faster: Shorten the loop from insight to experiment. Run feedback interviews in parallel and keep findings, prototypes and decisions in shared context, so each iteration starts with what the team already knows.
Alis Ideate Interviews · Alis Ideate Projects · Skill Store
Safer: Back roadmap bets with evidence. Make assumptions, constraints and success measures explicit. Keep decisions linked to customer input and involve engineering before a prototype becomes a delivery commitment.
Alis Ideate Projects · Alis Ideate Artifacts
Use cases
Discovery & decisions
Continuous discovery
- Build a regular customer interview loop around a product outcome
- Understand where users get stuck in a critical journey
- Bring customer, sales and support evidence into one opportunity brief
Tools: Alis Ideate Interviews · Alis Ideate Projects · Alis Ideate Artifacts
Start with the team's desired product outcome. Ask about recent customer behaviour and concrete examples, using Alis Ideate Interviews to broaden research and follow up. Identify whose perspective is missing and avoid presenting a small convenience sample as representative of all customers. Use supplied sales and support evidence rather than assuming integrations.
Opportunities & prioritisation
- Compare customer opportunities against the outcome you need
- Expose the riskiest assumption behind a roadmap bet
- Explain a prioritisation decision with supporting evidence
Tools: Alis Ideate Projects · Alis Ideate Artifacts · Skill Store
Separate customer problems, proposed solutions and assumptions. Consider value, usability, technical feasibility and business viability. Create a concise decision artifact that shows evidence, uncertainty and trade-offs. Use reusable discovery skills to structure judgement, not to manufacture precise prioritisation scores from weak data.
Prototype & concept tests
- Test alternative solutions with a working prototype
- Interview customers about what they tried in a prototype
- Agree what an experiment must teach you before building it
Tools: Alis Ideate Interviews · Alis Ideate Artifacts · Alis Build Workstations
Define the assumption, participant group and evidence needed to make a decision. Use a prototype at the appropriate fidelity and gather customer feedback, including screen sharing where useful. An interview does not automatically capture product analytics or run an A/B test. Keep real customer data out of unreviewed prototypes.
Experiments & delivery
Product trio collaboration
- Give product, design and engineering the same discovery context
- Turn a validated concept into a small, testable delivery brief
- Resolve feasibility and business constraints before committing
Tools: Alis Ideate Interviews · Alis Ideate Projects · Alis Ideate Artifacts
Involve product, design and engineering together. Capture the customer problem, evidence, constraints, acceptance criteria and open decisions in a shared project. Propose a small increment and the review needed. Avoid a handoff model in which a generated specification is treated as complete or automatically approved by engineering.
Build to learn
- Build a working experiment around the riskiest assumption
- Give a coding agent the evidence behind the feature
- Take a validated prototype through the team's release checks
Tools: Alis Ideate Projects · Skill Store · Alis Build Workstations · Alis Build DBD
Choose the smallest experiment that can address the uncertainty. Supply evidence and implementation constraints to the coding agent. If releasing to real users, use engineering-approved environments, tests and human release approval. Identify analytics or experiment tooling that must be integrated; AlisX is not itself an A/B testing or product analytics service.
Release feedback & outcomes
- Interview users after a release to shape the next increment
- Understand why a feature is not delivering the expected outcome
- Turn an experiment review into a reusable learning practice
Tools: Alis Ideate Interviews · Alis Ideate Projects · Skill Store
Combine interviews with actual usage and outcome data supplied by the team. Compare findings with the original hypothesis, consider alternative explanations, and decide whether to iterate, stop or expand. Capture the method as a reusable skill while preserving the evidence and uncertainty for this specific decision.
Invitation
Bring the product decision you need better evidence for. An onboarding journey that loses users. A feature request with an unclear payoff. A promising prototype. Start with the assumption that matters most and design the next step around what you need to learn.
Their AI's prompt