Optimafly. A Multi-City Optimized Flight Planner

Product Goal

The goal of OptimaFly was to help users spend less time planning and more time deciding. From a UX perspective, that meant

• Making trip setup intuitive even with multiple constraints Users can specify time windows, must-see vs. optional cities, and min/max stay days; the interface had to make these inputs clear without feeling like a form.
• Supporting flexible destinations and time windows without overwhelming users The product lets users choose a set of multiple cities and a date range instead of a single fixed itinerary. The design challenge was to surface that flexibility so it felt helpful, not overwhelming.
• Presenting ranked itinerary results in a way that made tradeoffs easy to scan and compare Results are ordered by cost and feasibility; the UI needed to surface key tradeoffs (price, dates, number of legs) so users could quickly choose among options.

Process

1. Build a working first version

   I built an initial full-stack version of OptimaFly to validate the core optimization workflow in a real product environment.

2. Test the first iteration early

   I ran usability testing with 10 student users to observe how they entered trip constraints, interpreted results, and compared itinerary options.

3. Identify patterns and pain points

   I synthesized recurring issues into user personas and behavior patterns, including differences in how users prioritized cost vs. convenience.

4. Redesign key UI flows

   Based on testing, I revised input patterns, information hierarchy, and results layout to reduce confusion and improve scannability.

5. Implement and refine

   I shipped the updated interface directly in the product and continued improving responsiveness through backend optimization work.

Design Approach

Design was iterated directly in code with React and HTML/CSS, no separate Figma phase, so that layout, copy, and interaction could evolve in lockstep with the backend API and optimization logic. Focus was on a single primary flow, set constraints, run search, scan results and compare. Reducing steps and clarifying labels (e.g., “must visit” vs. “optional”) helped keep trip setup intuitive; result cards were structured to highlight total cost, dates, and leg count so tradeoffs were scannable at a glance.

Personas & user research

We conducted 10 user interviews to observe how people entered trip constraints, interpreted results, and compared itinerary options. The persona we focused on is the student traveler. Students want to experience the world but are limited by budget, since many are not working yet, and constrained by time, such as school breaks. The design iterations below were shaped by this context and by what we learned in those interviews.

Design Iterations

These iterations were driven by user interviews and testing, so the product better matches what users asked for and how they behaved. Key iterations addressed results readability and engagement.