Ai + Design
AI powered content personalisation for travel
AI powered content personalisation for travel
You’re booking your next holiday or travel adventure, and the itinerary is coming together. You’re excited, right? But then you land on a checkout page or get a receipt email.
It’s almost impressive how anyone can make the thrill of travel feel so … mundane. Booking a trip should be wonderful, but these interactions somehow manage to drain away the magic.
Instead of adding value by offering something meaningful, they are added as an afterthought. They're boring, generic and uninspiring. The engagement rates
Why don't they work? They probably don’t know you, your preferences or what would actually make your trip better. Yet even if they do, and you’re a regular customer, they have few tools to suggest things that are compelling and personal specifically for you.
It just isn’t scalable. At least, it wasn’t.
The opportunity for travel businesses is to harness AI to craft engaging experiences unique to each user’s particular context, offering a level of creativity and personalisation that would be impossible without LLM’s.
*Yes, AI is a buzzword, but here’s what makes our approach unique. Large Language Models (LLMs) are often used generically—think chatbots everywhere. But as AI students, we understand the real potential and nuances of LLMs: where they excel and where traditional algorithms are better suited. In this project, we’ve strategically applied LLMs to tackle advanced personalisation, creating a uniquely tailored travel experience that goes far beyond simple recommendations.
The program begins by combining any existing user information—such as travel preferences, group size, or past behaviour—with real-time data about the searched destination, including weather, hotel availability, popular activities, and cultural factors.
Using the combined input, the system builds a more detailed travel persona. This includes generalising traits and expanding on inferred needs, like budget considerations, temporal experience, or language support.
Finally, the program generates a customised post-search checkout page. This page includes tailored recommendations for accommodations, activities, health and safety tips, required travel documents, and local transport options—curated to align with the user's persona and destination data.
A Figma prototype was iteratively developed alongside the backend, beginning with wireframes to establish layouts and design patterns. Internal reviews refined the high-fidelity mock-ups to create a polished, responsive, user-centric interface.
Currently does not interface with the backend, and exists to showcase how it could look.
This is the default text value
With guidance from a leading AI researcher in Australia, we created a rapid mockup using Wordware.ai, feeding a set of inputs (name, age, and nationality) into LLMs to extrapolate detailed personality traits and preferences and match users with accommodation.
While it technically functioned, it also revealed a hard truth: our solution offered no real advantage over existing recommendation algorithms.
As we investigated existing systems more deeply, we discovered large companies had already built sophisticated algorithms to match users with destinations, accommodations, and activities and our assumption that travel checkout pages are ineffective and fail to leverage the extensive customer data they collect was incorrect.
While existing recommendation systems are smart, they lacked personal context, so our new focus revolved around each users unique context. For example, a solo traveller from (warm) Singapore would prepare for Norway very differently than a family from (cold) Canada; their interests, packing needs, and essential information could vary significantly.
This led to a redefined process:
Try Wordware.ai to explore our personalisation features—test with premade personas or input your own context for a tailored experience.
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