INDUSTRY:

TRAVEL

TYPE:

MOBILE

TOOLS:

FIGMA

Wayfarer.

Wayfarer.

The context.

A travel planning experience that turns preferences into personalized, curated trips.

Wayfarer is a travel planning concept focused on helping people discover authentic, personalized experiences, not just destinations.

Instead of overwhelming users with options, the product guides them through a short, structured flow to understand how they like to travel, then generates tailored recommendations.

The problem.

Travel planning is usually fragmented and exhausting:


  • Too many options, not enough relevance

  • Generic recommendations across platforms

  • Endless scrolling with little personalization

  • Most tools optimize for booking and not for helping people decide where and how to go.

The process.

The instinct was to create a design system that was different from current travel booking platforms. I went with a deep purple for exploration. Lime for action, energy, a young audience of travelers, and CTA. The thought of adventure and exploration immediately conjured images of hot air balloons. Therefore, they became a metaphor for exploration, expansion, and transformation.

Insight.

People don’t want more options, they want fewer, better ones that feel personal.


Travel decisions are emotional. Therefore, users need guidance that reflects:


  • how they like to explore

  • what experiences matter to them

  • how much structure they want

Solution.

Wayfarer reframes trip planning as a guided, preference-driven experience.


Key ideas:

  • Short onboarding to understand travel behavior

  • AI-driven recommendations based on intent, not just filters

  • A journal system that feeds future personalization

The reflection.

This project focused on designing for decision-making, not just interaction.

The biggest shift was moving away from feature-heavy design and toward guided, intentional experiences.


Future iterations would explore:

  • deeper AI personalization

  • real-time trip adaptation

  • richer journal-to-recommendation loops

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