COAST
Student Project
Smart port management platform that uses AI to cut emissions, reduce costs, and boost efficiency using existing infrastructure.
MY ROLE
Project Manager
UI Designer
DURATION
20 weeks
TOOLS
Figma, Figjam, Rhino, KeyShot, Photoshop, Illustrator, ChatGPT, Perplexity
COLLABORATORS
Kaleigh Mackey, Austin Joseph, Pingyao Wan, Krishna Aggarwal, Emiliano Mirafuentes Resendez
Highlights
TL;DR
COAST (Clean Oceans and Sustainable Transport) is an AI-powered energy optimization platform designed to help U.S. seaports reduce emissions, eliminate fuel waste, and increase operational efficiency—without overhauling existing infrastructure. By combining simple, real-time energy-tracking hardware, Axons, with a centralized AI dashboard, Neuro, COAST empowers port operators to control energy use, monitor sustainability metrics, and make smart, data-driven decisions with ease.
0
users tested across both the app and physical device
0%
average success rate of product quality testing
0
major design iterations made based on usability insights from testing
Attach Axon to the equipment's main power line
Data is sent from Axon to Neuro via cloud IoT system
Manage equipment's live status, data, and reports on Neuro 24/7
Challenge
PROBLEM STATEMENT
U.S. Seaports are stuck using environmentally damaging practices for their daily operations, like gasoline-powered vehicles and machines, unsure of how to begin electrifying their day-to-day.
EXISTING USER PAIN POINTS
Overwhelmed by too many skincare options
Influenced by unreliable sources like social media
Wasted money on products
Trial-and-error approach to skincare
Lack of access to personalized skincare guidance
Skin appearance causes insecurity
OUR GOAL
Create a seamless, science-backed skincare experience that makes informed choices effortless and accessible.
Research
RESEARCH GOALS
GOAL #1
Understand how users choose skincare products and what/who may influence those decisions.
GOAL #2
Identify common sources of confusion and frustration in users’ skincare journeys.
GOAL #3
Explore user expectations and desires for skincare predictive technology.
METHODS
10
weeks of secondary research
8
in-depth user interviews
5
concept redirections
10
usability tests
USER PERSONAS
USER JOURNEY MAP
Holistic journey
Product journey
?
HOW MIGHT WE…
empower people to confidently navigate their skincare journey without being swayed by unreliable trends?
create a sustainable, informed approach to beauty?
Ideation
INFORMATION ARCHITECTURE & USER FLOW
SERVICE MODEL CANVAS
LOW FIDELITY SCANNER
INITIAL UI SKETCHES
LOW FIDELITY UI
Onboarding
Initial skin scan
Results and suggested products
Homescreen
Iteration
MID-FIDELITY SCANNER
INITIAL DIGITIAL PROTOTYPE
MID FIDELITY UI
Onboarding
Initial skin scan
Results and suggested products
Homescreen
Build a routine
User Testing
TESTING RESULTS
Round 1
10%
success
60%
difficulty
Round 2
30%
success
40%
difficulty
Round 3 — Final
70%
success
5%
difficulty
90%
found Vega easy to use and integrate into their routine.
80%
said it would help them make better skincare purchases.
100%
felt more confident in their skincare choices after using Vega.
KEY DESIGN ITERATIONS
Simplified scanner design to fit gua sha shape
Repurposed trigger button as a system status indicator
Device will automatically begin analyzing skincare sample once it senses an input
introducing
VEGA SCANNER
VEGA APP
Streamline your routine to keep only what you need
Get what you need for your current skin state
Learn more about what is happening with your skin
Learn about recommendations
Explore more about an specific product and where to buy
Control the Vega scanner and set up reminders to scan skin
VISION VIDEO
What did we learn?
We have a real winning concept here
Everyone we tested and interviewed was super excited about the concept of personalized skincare matching.
Clear affordances and feedback builds user trust
We learned how crucial it is to design affordances that are intuitive, noticeable, and aligned with user expectations. Form and function can’t be separated.
Users need gentle guidance
Even with a clean app and device, some users still need nudges to know what to do next. We spent longer designing an informative onboarding and product manual to lower the chance of confusion.