Our vision for seamless restaurant discovery and booking integrates AI-powered search with an intuitive interface, helping you find the best dining spots based on real user reviews. This project aims to make each step, from search to reservation, effortless and personalized, with smart recommendations always within reach.
Gather insights on user needs and behaviors through interviews and Jobs-to-be-Done (JTBD) framework. Analyze competitors to identify strengths, weaknesses, and opportunities for differentiation.
Define the app's core functionalities based on research findings. Prioritize features to align with user needs and business objectives, ensuring a seamless booking experience.
Develop wireframes and high-fidelity prototypes, focusing on intuitive navigation, accessibility, and AI-driven recommendations. Establish a strong visual identity with well-balanced typography and color schemes.
Conduct usability testing on prototypes to validate design decisions. Analyze user feedback, refine interactions, and improve overall usability before final implementation.
Do you remember the last time you searched for a restaurant?
What steps did you take?
What apps or websites did you use for this? Why did you choose
them?
In general, what is more convenient for you when searching for restaurants: using
apps, recommendations from friends, or other methods?
What type of venues do you visit most often (cafes,
restaurants, bars)? Why?
How do you usually find a suitable venue through an app?
What filters do you use?
What additional filter options could make your search
easier?
Have you ever encountered unreliable reviews? How do you
determine if they can be trusted?
What review formats do you find most useful? Why?
(Video, text, photo)
What usually stops you when you can't decide on a restaurant?
How do you solve this problem?
How do you choose new places when traveling?
What does
your ideal restaurant selection process look like? What would you add or remove?
How often do you use recommendations in apps? Do they meet your
expectations?
What data should an app consider to make recommendations more personalized
for you?
Our design system implements a three-tier color token architecture:
This hierarchical approach ensures systematic color application while maintaining flexibility for theming and scalability across products and platforms. The token structure enables consistent visual language while allowing controlled adaptation to different use cases.
Decision: Emphasis on restaurant search with intuitive filters, large restaurant cards, and AI-powered interaction. High-quality images make the experience more engaging.
Why: Users rely on visuals and well-structured information when choosing a place. Large cards improve readability, showcase key details at a glance, and AI suggestions help streamline the decision-making process.
Decision: Detailed restaurant information – description, map, rating, reviews, and similar places.
Why: Users trust restaurants with clear, structured information, and the "Similar Places" section helps retain engagement.
Decision: AI-powered assistant for restaurant search, booking, and personalized recommendations. It also features a simplified chat interface that helps users quickly compose messages and interact effortlessly.
Why: Users often spend too much time choosing a restaurant. Ulti simplifies the process by suggesting relevant options, handling reservations, and providing a seamless, guided experience.
