AI-First Trivago Concept
Reimagining hotel search from the ground up: what if Trivago was built entirely around AI?
| OVERVIEW
This concept project reimagines Trivago as if it were built from the ground up as an AI-native platform, not just enhanced with AI features.
Instead of layering AI into existing UX patterns, the goal was to invent a new paradigm: one where AI becomes the core interaction model by guiding decisions, adapting to intent, and reshaping how people search, compare, and book hotels.
The result is a speculative product vision that challenges how we design for travel discovery in an AI-first era.
Why I did this:
To inspire future product thinking and spark internal discussion on what Trivago could become if rebuilt as an AI-native travel platform.
| THE CHALLENGE
I was tasked with imagining how Trivago could shift from being a traditional hotel metasearch engine into an AI-first product where the conversation with the system becomes the product.
This meant:
Rethinking the user journey, from query to booking.
Designing interface models that flex with user intent.
Centering the experience around AI guidance, not static filters or linear flows.
Creating a bold, inspirational concept, unconstrained by technical limitations or current design systems.
| STARTING POINT
I began by auditing Trivago’s current search experience:
Linear, input-driven, and static
No adaptation to user context, goals, or uncertainty
Filters and sorting dominate, rather than guided exploration
In parallel, I examined AI-native products across other industries (from ChatGPT and Anthropic to Google’s Search Generative Experience) to identify how expectations are shifting.
What stood out:
These products don’t require users to know exactly what they want upfront. Instead, they help shape the journey through conversation, visual cues, or adaptive flows.
The contrast revealed a gap and an opportunity:
Trivago still treats search as a transaction, while AI-first models act more like partners in discovery.
| FRAMING THE OPPORTUNITY
I defined key design questions:
How can AI anticipate or clarify ambiguous intent?
How should the system respond differently to “a hotel in Tokyo” vs. “a summer trip in July”?
What if the interface adapted in real time to what the user says?
| OPPORTUNITY
By reimagining Trivago as an AI-first product, we can guide users through their search with smart, conversational support. This creates value for users and the business: more relevance, better conversion, and a travel experience that feels truly modern.
Most people don’t know exactly what they want when they start. With AI, we can meet them earlier, help them decide faster, and build trust through adaptive suggestions and timely guidance.
How the experience shifts
Current Trivago
Long, generic hotel lists (200+ results) |
User must know exactly what to search |
Static filters and sorting |
Same UI for every scenario |
Relevance depends on manual input |
No reason shown for recommendations |
Decision support is buried in tabs |
Emotionally neutral |
Search = transaction |
Proposed AI-First Trivago
Curated suggestions (3–5 hotels per step) |
Users can start with a vague idea or vibe |
Dynamic, adaptive chips based on intent |
Modular layout that adapts to the journey |
Relevance guided by conversation context |
Each hotel shows a “why picked” tag |
Detailed information appears when needed |
Conversational, helpful, human-centered |
Search = partnership in discovery |
This led to a core structural idea:
A three-panel system that adapts to the user’s scenario:
Panel 01
Conversation & Input
The entry point for the user’s intent. This panel handles the dialogue with the AI guiding the user, asking follow-up questions, and offering contextual suggestions.
It’s how users communicate what they want.
Panel 02
Discover & Compare
The main area for browsing and decision-making. This panel adapts based on the scenario: showing hotel lists, inspiration cards, or hotel detail pages.
It’s where users explore and narrow down options.
Panel 03
Contextual Details
The flexible panel for deeper insights. This panel supports what’s in Panel 2, always tailored to the user’s current focus. It shows room details, booking providers, reviews, maps, or saved lists.
It’s how users decide what to do next.
| SCENARIO BASED DESIGN
I defined some core user intent scenarios and mapped custom flows for each.
Search-driven:
No Destination
User shares time or goal, but not a place.
→ e.g. “Hotels for a summer vacation in July”
Region + Theme
User asks for experiences across a broad area.
→ e.g. “Trip to Greece with pool and spa to relax”
City (Standard)
User enters a city name with no specific focus.
→ e.g. “Hotels in Tokyo for October 4 to 6, 2 guests”
City (Specific)
User mentions price, traveler type or needs.
→ e.g. “Solo traveler cheap hotels in Amsterdam”
Point of Interest
User names a landmark, airport, or known place.
→ e.g. ““Hotels near Heathrow Terminal 5”
Specific Hotel
User asks for a specific hotel by name.
→ e.g. “I want to go to The Plaza in New York"
System-initiated:
New User
User needs help starting.
(No input, shown general suggestions)
Returning User
User returns to a previous query or flow.
(No input, shown personalized suggestions)
For every scenario, I designed:
A tailored AI conversation flow
Chip suggestions for refinements
Modular card systems
Adaptive panel behavior and content logic
| WHERE I LANDED
Key Concepts:
01
AI as interface
The primary way to search and decide
02
Soft guidance
Via conversational chips and tags
03
Scenario-driven UI
Not one-size-fits-all
04
Contextual decision support
Details appear when needed
| SOME MOCKUPS
The Plaza Hotel in New York
The user knows exactly where they want to stay. They ask for The Plaza in NYC, and the system responds with availability, room types, and curated tags showing what stands out (e.g. “Timeless Luxury” or “Unmatched location”). The flow remains guided, even with specific input.
Girls’ trip to Greece
The user begins with a conversational prompt: a girls’ trip to Greece with pool and spa. The AI responds with a visually rich set of Greek island suggestions, each showing weather, pricing, and vibe tags. The user explores options, selects a hotel in Paros, and then asks the AI to show similar stays. The interface seamlessly transitions into a curated list of hotels that reflect the same mood, each with a clear reason for why it’s being recommended.
Layover near Heathrow
The user asks for a hotel near Heathrow Airport for a one-night layover. The AI understands this as a time-sensitive need, shows options near terminals, and pulls up a map to support the choice. The user then asks the assistant to pick one, leading to a focused hotel detail view.
| OUTCOME OF PROOF OF CONCEPT
Multiple high-fidelity mockups across key scenarios
A logic for adaptive panels, prompts, and suggestions
A vision for how Trivago could guide users, not overwhelm them
| WHY IT MATTERS
AI as the product, not a feature
This exploration is meant to reframe AI as the foundation of the experience, not just an enhancement. It aims to spark a mindset shift toward building with AI at the center.
New standards for interaction
It is meant to open internal conversations around where interaction is headed: voice, chat, inline nudges and how Trivago should evolve.
From execution to provocation
This wasn’t about building something tomorrow. It was about challenging assumptions and pushing the vision forward.
