INTERACTIVE PRODUCT MODELSimulated interface mechanics demonstrating core problem framing & systems logic
FARELY // OPPORTUNITY INTELLIGENCE
CORRIDOR: HAN ⇄ NRTWINDOW: 30-DAY HISTORICAL MEDIANNET DURATION OPTIMIZED
HANOI (HAN)─── 5h 25m Direct ───►TOKYO (NRT)
$440 30d median$284 observed
PRICE CONTEXT-35% BELOW MEDIAN
NET TRAVEL EFFICIENCYDIRECT (NO LAYOVER)
OPPORTUNITY STATUSVERIFIED CANDIDATE
00:30 HANDeparture Noi Bai T2
07:55 NRTArrival Narita T1
01 / The Challenge & Context
The Operational Problem
Travel search engines frequently promote low prices that hide 30-hour layovers, extra airport transfers, or manufactured urgency tricks like 'Only 1 seat left at this price!'
02 / System & Design Architecture
User Flow & Ergonomics
Route graph traversal → Net travel duration evaluation → 30-day historical fare baseline comparison → Deliberate opportunity notification.
KEY TRADE-OFF // NET DURATION AS THE PRIMARY QUALITY METRICA cheap ticket with an exhausting 28-hour layover is not a bargain. Farely penalizes excessive layovers in its discovery engine.
03 / Verifiable Outcome & Learning
Impact & Retrospective
Users evaluate route trade-offs faster and avoid deceptive itineraries with punishing layovers.
LEARNING:Authentic utility builds loyalty. When an interface eliminates fake countdowns, users trust its actual deal recommendations.
[CURRENT CAPABILITIES]
Route graph traversal optimizing net travel time
Historical fare band comparison against 30-day median prices
Zero manufactured scarcity (no fake countdowns or fake urgency pills)
Clear visual timeline breakdown of transfers and airport dwell times