Featured work

02 / Local discovery / weather-smart product

Trauvo

A weather-aware discovery experience that helps people find something worth doing nearby, across British Columbia.

RoleProduct design + mobile development
PlatformiOS · Apple Watch
Year2025
StackSwift · PostgreSQL · CatBoost · Core ML
LEAPER / 02 Featured work
L18 SUGGESTIONS / DAY
01Weather changes the answer
02iOS · Apple Watch

THE PRODUCT

Trauvo is a weather-smart local discovery product for cities and destinations across BC. It gives the day a manageable set of suggestions, then adds context such as crowd advisory, date ideas, and a practical Pro Tip.

THE PROBLEM

Local discovery is usually either a directory or an endless feed. Trauvo was shaped around a more useful question: given the place, weather, and moment, what should I do now?

MY ROLE

I worked across the onboarding, suggestion card, review, Pro Tip, crowd advisory, date category, and Apple Watch experience. The work focused on making the product feel like a calm decision tool rather than another content feed.

PRODUCT DECISIONS

01

Limit the daily set of suggestions so discovery has a rhythm instead of becoming infinite scrolling.

02

Treat weather as a product input that changes the recommendation, not as a decorative forecast widget.

03

Make the suggestion card carry enough context to support a decision without opening several screens.

04

Extend the product to the Apple Watch for quick, glanceable discovery moments.

INTERFACE

The interface uses large suggestion cards, concise context, and clear transitions between deciding, exploring, and reviewing. Each screen is designed to reduce the work of choosing.

ENGINEERING

The product combines native mobile experiences with a backend-connected recommendation system. PostgreSQL supports the product data, while CatBoost and Core ML were explored for smarter, context-aware suggestions.

KEY FEATURES

18 daily suggestions shaped around place and conditions
Apple Watch companion experience
App Clip and streamlined onboarding
Crowd advisory and Pro Tip context
Date category for more intentional local plans

TECHNOLOGY

Swift · PostgreSQL · CatBoost · Core ML

WHAT CHANGED

Personalization is more useful when it removes decisions, not when it adds more content.

NEXT PROJECT

03Konekt