A new way to decide
what to eat
Lite Bite Subs · Concept Project · 2025
Role in the project
Designed the product from scratch, including UX, UI, and branding.
The project evolved through self-initiated iterations, introducing new features such as AI-assisted recommendations and order code generation.
Problem
People who want to eat healthier often struggle to understand nutritional information when ordering food.
Even when data is available, it’s hard to compare options or customize meals based on personal goals.
Concept
Instead of browsing menus, users describe what they want, and the system generates a personalized sandwich.
This shifts the experience from searching to deciding.
Initial Approach
Version 1: Information-heavy
Features
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Detailed nutrition shown directly in the menu
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Clear breakdown of calories and nutrients
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Focus on transparency and data visibility
What could be improved?
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Limited number of items visible at once
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Hard to compare multiple options side by side
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Requires users to interpret and evaluate information manually
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Slows down decision-making, especially for quick choices
Version 2: Structured and scalable
Features
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Simplified nutrition overview for quicker scanning
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More items visible within a single screen
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Improved navigation with bottom menu
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Cleaner layout that supports faster browsing
What could be improved?
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Still relies on users to compare options manually
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Nutritional data is reduced, but not interpreted
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Decision-making remains effort-based rather than guided
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Does not fully support goal-oriented or preference-based choices
Even with a more refined interface,
the core issue remained.
The product was still built around browsing and comparing, rather than helping users decide.
This led to a shift in approach.
New Feature: AI Meal Assistant
Why AI?
Nutrition data alone doesn’t make decision-making easier.
Users still need to interpret information and compare options.
This raised the question:
what if the system could interpret that information for them?
What it does?
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Turns user input into a direct, personalized meal suggestion
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Eliminates the need for manual comparison
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Guides users toward faster, more confident decisions
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Responds to dietary needs and personal goals
Once a suggestion is generated, users can add it to their meal and continue building their order.
From suggestion to order flow
After receiving a suggestion, users can add items to their meal and continue selecting from the menu.
This allows them to build a complete order based on both recommendations and personal choices.
Once ready, users can generate an order code that represents their full meal, making it easy to place the order in-store.
