AI Booking project introduces an AI-powered booking assistant designed to simplify that process by guiding users through their trip planning in a natural, conversational way. Instead of navigating complex interfaces, users can describe their needs and receive personalized recommendations instantly.

For the MVP, the platform focuses on hotel bookings, allowing users to search and reserve accommodations through a seamless chat flow. The long-term vision expands this assistant into a complete travel ecosystem that supports Flights, Things To Do, Events, Car rentals and more - all managed within a single interface.

As the UI/UX designer on this project, my goal was to translate a complex, multi-step booking workflow into a seamless, human-like dialogue powered by AI.

Key Contributions:

• Conducted user research to uncover pain points in travel planning and AI expectations.
• Performed a competitive analysis of major booking platforms to identify gaps, opportunities, and differentiators for an AI-first experience.
• Defined the product vision and MVP scope using a Lean Canvas.
• Designed the AI-driven booking flow, mapping how the assistant collects user intent through conversation.
• Created clear, minimal UI screens for the hotel-booking MVP.
• Built the booking funnel to visualize user interactions and friction points.
• Ran usability testing, analyzed CSV logs, and identified behavior patterns and drop-offs.
• Summarized findings into actionable insights and recommendations for improving the next iteration.

Context

Today, users often jump between multiple apps to compare hotels, flights, and activities, creating a fragmented and time-consuming experience. Our goal was to replace this complexity with a seamless, assistant-driven flow where users could plan an entire trip through a natural conversation.

We first validated the concept through targeted user research to uncover key pain points in the travel planning journey. These insights helped define the core expectations for the AI assistant: clarity, personalization, and guided support from planning to booking.

To understand the market and identify opportunities for differentiation, I conducted a focused competitive analysis of existing AI-assisted travel platforms. This research highlighted common usability gaps, limitations in personalization, and breakdowns in end-to-end support—insights that directly informed the scope and priorities of our MVP.