Y2Z Travel
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Onboarding completion
As Founding Product Designer, I worked alongside the CEO on the 0-to-1 roadmap, leading everything from early field research to design and GTM strategy while balancing bootstrapped constraints.
Overview
Background
Every piece of the trip—ideas, notes, tickets and expenses lived in a different app. What we were missing was traceability. Notes tied to a place. Research tied to a day. Context we could track without losing it or hunting across a dozen tools.
We suspected the setup was bad. The data proved it. Our app activity logs showed that a single day of trip planning involved over 220 instances of opening travel-related applications, constant context-switching across a dozen apps that exposed the real gap.
We researched what tools people currently use to plan journeys, and tried the fixes ourselves - Stippl, Notion and our own Sheets hack.
Every option forced the same trade. Usability or flexibility, never both. Stippl sat closest to usability. No tool occupied the zone we needed, the Ideal Zone.
Four tiers, each testing the same question from a different angle: social signals, macro data, field immersion, and synthesis, so no single source could carry a false signal on its own.
This research was compiled from 50+ traveler interviews, 30+ industry reports, financial market reports on travel, conversations with travel agents and analysis across online communities.
Research & Discovery
Field research, synthesis, and interviews surfaced more friction than expected. We mapped 70+ findings it into a sunburst, clustering insights by relevance to trace each one back to its root.
The Challenge
Here's a version folding that in:
As a team, we ran affinity mapping to group the research data against our personas, helping us surface the most critical problems alongside the initial direction, which collectively shaped the HMW questions shown below.
Challenge 1
Challenge 2
Challenge 3
Process
Personas were built grounded in real user data, then the journey was mapped across four stages: inspiration, research, planning, booking.
Story map kept us anchored to those needs, ensuring every current and future feature stayed aligned to what users actually needed.
To solve the 90-day fragmentation problem, AI was the clear answer. The reports agreed: businesses across the industry were expected to build generative AI into their offerings given the scale of demand.
We planned to build AI as our foundational model long-term, but for the MVP, we used a third-party API from OpenAI, tuning prompts from the parameters collected during the interview to power the system in the early stage.
To solve the 90-day fragmentation problem, AI was the clear answer. The reports agreed: businesses across the industry were expected to build generative AI into their offerings given the scale of demand.
We planned to build AI as our foundational model long-term, but for the MVP, we used a third-party API from OpenAI, tuning prompts from the parameters collected during the interview to power the system in the early stage.
Mapping the interface, we explored usable formats, staying close to the core needs around maps and travel unity. We tested multiple options, then selected the direction based on what travelers actually quoted during interviews.
Attachments were considered by default, with similar information grouped together in the process.
Personalisation needed a lot of data, hard to get upfront. The turnaround: collect it through onboarding, before generation even starts. Among the options explored, this worked best, a smoother UX and better input for the AI to build the itinerary around.
Early Explorations
To solve the 90-day fragmentation problem, AI was the clear answer. The reports agreed: businesses across the industry were expected to build generative AI into their offerings given the scale of demand.
We planned to build AI as our foundational model long-term, but for the MVP, we used a third-party API from OpenAI, tuning prompts to power the system in the early stage.
Mapping the interface, we explored usable formats, staying close to the core needs around maps and travel unity. We tested multiple options, then selected the direction based on what travelers actually quoted during interviews.
Attachments were considered by default, with similar information grouped together in the process.
Personalisation needed a lot of data, hard to get upfront. The turnaround: collect it through onboarding, before generation even starts. Among the options explored, this worked best, a smoother UX and better input for the AI to build the itinerary around.
Solution
When Midjourney approached us, they already had a bbrowser experience but needed a dedicated space for users to connect, share, and learn.
When Midjourney approached us, they already had a bbrowser experience but needed a dedicated space for users to connect, share, and learn.
Results
Mapping the interface, we explored usable formats, staying close to the core needs around maps and travel unity. We tested multiple options, then selected the direction based on what travelers actually quoted during interviews.
Attachments were considered by default, with similar information grouped together in the process.
