[Case 02]
AT-A-GLANCE
TravelGPT (formerly VOYA) is an AI-powered itinerary builder that reimagines how travel agents plan and personalize trips. The mission was to help agents go from hours of manual planning to an intelligent, collaborative experience where they could co-create itineraries with AI.
The challenge: designing for a technology (agentic AI) that was still being defined; while grounding it in real-world UX principles and trust.
[Industry]
Travel Planning
[My Role]
Lead UX Designer
[Platforms]
Desktop
[Timeline]
October 2024 - Present
Objectives
Redefine itinerary creation as a collaborative AI-human workflow.
Build an interface that bridges system reasoning with human decision-making.
Reduce manual effort and improve planning accuracy, transparency, and speed.
Key Outcomes
90% reduction in itinerary creation time (5 hours → under 5 minutes).
78% of beta users described the experience as “magical.”
Served as a foundation for TBO’s AI strategy and luxury B2C travel roadmap.
Who are we solving for?
[Image: create with AI an image of a travel agent juggling multiple tasks at once]
Travel agents are professional intermediaries who design, plan, and manage trips for individuals, families, and organizations. They combine market knowledge, supplier networks, and human judgment to curate travel experiences that match clients’ needs and budgets. In an era dominated by online booking, travel agents remain essential because they provide personalized guidance, handle complex itineraries, and offer trusted post‑booking support. TBO’s user base includes these professionals globally — from independent consultants to enterprise agencies — serving leisure travelers, corporate clients, and luxury seekers alike.
Designing for TravelGPT meant building for a diverse, international ecosystem of travel experts, each with distinct workflows, cultural contexts, and technological maturity levels.
The current state of travel planning
In most agencies today, travel planning remains a heavily manual process. A typical travel professional works across spreadsheets, chat threads, and vendor portals—coordinating flights, hotels, transfers, and activities while juggling client preferences and supplier updates. Information is fragmented, and each new request requires a mix of copy‑paste operations, negotiation calls, and visual formatting. There are no dedicated tools that unify these steps or preserve context. Every itinerary becomes a small project in itself, reliant on the agent’s personal organization, intuition, and recall.
This fragmented workflow not only slows productivity but also leaves little time for what matters most: curating experiences that feel personal and memorable.
Background and Problem Definition
The Beginning
It all started with a vision: What if AI planned travel?
Since we were diving into uncharted waters of new technology within the travel planning domain, I organized a small design hackathon where the UX and product teams were divided into smaller groups. The goal was to think boldly, explore freely, and bring unconventional ideas to the table that tested the limits of what AI could do for travel.
This exercise helped us gather a wide range of concepts some practical, some wildly futuristic all of which allowed us to map overlaps and identify recurring pain points. From these, we distilled common problem statements that could define the foundation of the project moving forward.
[Image: Hackathon Designs images]
Early Validation
To validate the common problem statements identified during the design hackathon, I interviewed 10 travel agents across markets (India, UAE, UK), focusing on.
How they currently build itineraries
Which parts of their workflow they found repetitive
Where they felt “creative satisfaction” versus “manual frustration”
Competitor Benchmarking
I analyzed products like TravelPerk, Travefy, and TripHobo, studying their flows, UX patterns, and gaps:
Most tools were linear and rigid, optimized for travelers, not travel agents.
None allowed AI-powered collaboration or real-time itinerary structuring.
This uncovered our opportunity space — AI as an orchestrator, not just a formatter.
[Image: Competitor analysis]
Ideation & Early Wireframes
Armed with the insights, I brought the team together once again to share the research findings and key observations. We conducted a collaborative brainstorming session aimed at exploring a wide spectrum of design directions, encouraging everyone to sketch, critique, and iterate rapidly. The goal was to produce multiple ideas and perspectives on how the product could evolve. Some examples are:
Text-to-itinerary via prompt input
Visual card-based drag-and-drop assembly
AI auto-layout based on trip length and destinations
After capturing and synthesizing all ideas, I distilled the strongest concepts and moved forward into the design phase with clear, validated directions.
[Image: Snapshots of wireframes and sketches]
Identified Problem Statements
[Add image from presentation]
Outcome
I built the first version of VOYA, a 100% Figma prototype where I simulated the end-to-end user experience. Some of its core interface elements were converted into a working prototype using Lovable and Supabase, allowing us to test real interactions.
VOYA v1 was then taken into the field, where I spent two weeks in Mumbai working alongside travel agents in their offices — observing, testing, and participating in their real workflows.
This immersion helped uncover deep use cases, revealed gaps between design and daily operations, and captured authentic first reactions to how AI could fit into their existing routines.
[Video: Voya V1 Prototype]
Quick Summary:
Highlights
Began with a bold vision of “AI-planned travel” launched through an internal TBO design hackathon.
Conducted 10 validation interviews with agents, followed by multiple brainstorming rounds to refine problem statements.
Benchmarked competitors (Travefy, TripHobo, TravelPerk) to uncover unmet gaps.
Solution
Facilitated cross-team ideation sessions to explore diverse possibilities and identify recurring pain points.
Led design sprints that produced rough sketches, wireframes, and multiple concept iterations tested with real agents.
Built VOYA v1 — initially a Figma prototype with select components prototyped using Lovable and Supabase — and field-tested it for two weeks in Mumbai with travel agents.
Results
Established a shared understanding of travel workflow challenges across teams.
Captured authentic early feedback that guided the first working prototype — VOYA v1.
60% faster output, while field testing uncovered deeper challenges in the workflow that shaped the next phase.