Nice Right Labs · Understand

Interface Studies

Coded decision studies for the next Understand app: OOUX, ORCA, IA, IxD, YAGNI, trust, and product-led growth loops made tangible in working prototypes.

Current object model

Passage
├─ Original
├─ Retelling
│  ├─ Retelling Style / Persona
│  ├─ Fidelity / Source Distance
│  └─ Output Text
├─ Performance
│  ├─ Voice
│  ├─ Voice Capability
│  └─ Playback
└─ Listening Session

PLG Objects
├─ Sample / Proof Passage
├─ Shareable Retelling
├─ Continue Moment
├─ Upgrade Trigger
└─ Invite / Feedback Request

Full coded studies

built study

Retelling Style + Fidelity

Hypothesis: Users will understand the product better if Persona / Retelling Style and Fidelity are separate controls.

Decision: Should Retelling Style + Fidelity become the core transformation UI?

Open study →
built study

Passage-first Stage

Hypothesis: The app should organize around Passage, not Reader / History / Voice chrome.

Decision: Should Passage become the root app object?

Open study →
built study

Performance Capability

Hypothesis: Premium is more compelling when framed as expressive performance capability, not generic cloud voice quality.

Decision: Should premium be positioned around style-following expressive performance?

Open study →
built study

Sample Aha

Hypothesis: First-run should begin with curated proof passages before asking users to paste their own text.

Decision: Should samples be P0 onboarding objects?

Open study →
built study

Compare / Trust

Hypothesis: Serious texts need enough Original ↔ Understand comparison to create trust without turning the app into a study tool.

Decision: How much Original ↔ Understand comparison should the app expose by default?

Open study →
built study

Continue Loop

Hypothesis: History should become Continue / Library, not primary navigation.

Decision: Should History be reframed as Continue / Library?

Open study →
composed

Composed User Story

The working candidate flow that combines Passage, Retelling Style, Fidelity, Performance Capability, Continue, and PLG artifacts.

Decision: Can these models work together as the app direction?

Open prototype →

Candidate mental models not fully built yet

candidate

Retelling-first

Should the generated Retelling be the hero object?

Deferred unless the first studies show this model deserves a full route.

candidate

Fidelity-first

Should source-distance be the primary first choice?

Deferred unless the first studies show this model deserves a full route.

candidate

Persona / Medium-first

Should users choose the kind of telling before text?

Deferred unless the first studies show this model deserves a full route.

candidate

Coach / Guide-first

Should the app act like a tutor/guide?

Deferred unless the first studies show this model deserves a full route.

candidate

Narrator-first

Should a narrator/host become the product face?

Deferred unless the first studies show this model deserves a full route.

candidate

Workflow-first

Is import/process/save/share the main frame?

Deferred unless the first studies show this model deserves a full route.

Product-led growth lens

PLG is modeled as product objects, not marketing garnish. No growth ask appears before aha.

Sample
Try before paste.
Aha
Hear the retelling work.
Share artifact
Before/after retelling.
Continue
Resume what mattered.
Upgrade
Expressive performance boundary.