DINING APP

Empowering Stress-free,
Safe Dining Experiences

COMPANY

SafeEATS

ROLE

Product Designer

EXPERTISE

UX/UI Design

YEAR

2023

Project Description

SafeEATS is a mobile app that helps people with food allergies, medical/dietary restrictions, and religious/cultural food rules quickly find safe restaurants—without hours of guesswork. I served as a Product/UX Designer on a cross-functional Co.Lab team (product manager, 2 designers, 2 software engineers) to define the MVP, craft the end-to-end flow, and iterate the interface based on research and user testing.

Timeline

8-week product sprint: research → scope → design → prototype → implementation handoff → usability testing & iteration.

Background

Dining out is especially stressful for people with allergies and restrictions. The team’s early scan and interviews surfaced a persistent pattern: time-consuming research, uncertainty at the table, and limited options—all of which degrade the dining experience. SafeEATS set out to centralize trustworthy info and make “safe” choices fast and confident.

Process

This category details the step-by-step approach taken during the project, including research, planning, design, development, testing, and optimization phases.

Research & Planning

Recruitment: We sourced participants via social media; 13 users (allergies, medical, dietary, and cultural/religious restrictions) completed surveys/interviews.

Synthesized Pain Points:
1. Time sink finding viable restaurants
2. Can’t be spontaneous with friends
3. Uncertainty even when a venue is chosen.
  These drove our MVP scope.
Personas & Success Criteria: Three personas informed feature prioritization; we drafted confidence/experience metrics to validate impact post-search.

Recruitment: We sourced participants via social media; 13 users (allergies, medical, dietary, and cultural/religious restrictions) completed surveys/interviews.

Synthesized Pain Points:
1. Time sink finding viable restaurants
2. Can’t be spontaneous with friends
3. Uncertainty even when a venue is chosen.
  These drove our MVP scope.
Personas & Success Criteria: Three personas informed feature prioritization; we drafted confidence/experience metrics to validate impact post-search.

Recruitment: We sourced participants via social media; 13 users (allergies, medical, dietary, and cultural/religious restrictions) completed surveys/interviews.

Synthesized Pain Points:
1. Time sink finding viable restaurants
2. Can’t be spontaneous with friends
3. Uncertainty even when a venue is chosen.
  These drove our MVP scope.
Personas & Success Criteria: Three personas informed feature prioritization; we drafted confidence/experience metrics to validate impact post-search.

Design & Prototyping

IA & Flow: Focused the flow around two core paths—onboarding preferences → filtered results and nearby browse—while removing redundancies (e.g., a duplicate “Find a Restaurant” entry).

Usability-led UI Iterations:
1. Replaced dropdowns with inline multi-select chips to reduce scrolling and speed input.
2. Standardized fully rounded buttons and added more negative space for clarity.
3. Moved the Back control to the top-left and replaced a vague “Next” with a clearer CTA when inputs were complete.
4. Renamed “Saved” → “Favorite” and aligned iconography for recognition. joincolab.io

Artifacts: Lo-fi wires → hi-fi mockups (Figma) and an interactive prototype for testing.

IA & Flow: Focused the flow around two core paths—onboarding preferences → filtered results and nearby browse—while removing redundancies (e.g., a duplicate “Find a Restaurant” entry).

Usability-led UI Iterations:
1. Replaced dropdowns with inline multi-select chips to reduce scrolling and speed input.
2. Standardized fully rounded buttons and added more negative space for clarity.
3. Moved the Back control to the top-left and replaced a vague “Next” with a clearer CTA when inputs were complete.
4. Renamed “Saved” → “Favorite” and aligned iconography for recognition. joincolab.io

Artifacts: Lo-fi wires → hi-fi mockups (Figma) and an interactive prototype for testing.

IA & Flow: Focused the flow around two core paths—onboarding preferences → filtered results and nearby browse—while removing redundancies (e.g., a duplicate “Find a Restaurant” entry).

Usability-led UI Iterations:
1. Replaced dropdowns with inline multi-select chips to reduce scrolling and speed input.
2. Standardized fully rounded buttons and added more negative space for clarity.
3. Moved the Back control to the top-left and replaced a vague “Next” with a clearer CTA when inputs were complete.
4. Renamed “Saved” → “Favorite” and aligned iconography for recognition. joincolab.io

Artifacts: Lo-fi wires → hi-fi mockups (Figma) and an interactive prototype for testing.

Development & Implementation

Platform: Chosen as mobile-first (React Native) to meet users where the decision happens—on the go.
Backend: Firebase.
Tools: VS Code, Git/GitHub. Authentication was de-prioritized to focus on restaurant discovery and recommendations in the MVP. App submission targeted the Apple App Store (pending during sprint).

Platform: Chosen as mobile-first (React Native) to meet users where the decision happens—on the go.
Backend: Firebase.
Tools: VS Code, Git/GitHub. Authentication was de-prioritized to focus on restaurant discovery and recommendations in the MVP. App submission targeted the Apple App Store (pending during sprint).

Platform: Chosen as mobile-first (React Native) to meet users where the decision happens—on the go.
Backend: Firebase.
Tools: VS Code, Git/GitHub. Authentication was de-prioritized to focus on restaurant discovery and recommendations in the MVP. App submission targeted the Apple App Store (pending during sprint).

Testing & Optimization

Prototype testing revealed two key opportunities:

“Browse” ambiguity: 2 of 4 testers confused the post-filter browse view with the global nearby browse. We flagged copy/labeling and navigation refinements.
First-impression clarity: The home/login screen and the name “SafeEATS” alone weren’t descriptive enough; we recommended tighter value messaging on entry.
We also outlined in-flow feedback prompts (post-search rating à la Uber) to measure perceived confidence and experience quality.

Prototype testing revealed two key opportunities:

“Browse” ambiguity: 2 of 4 testers confused the post-filter browse view with the global nearby browse. We flagged copy/labeling and navigation refinements.
First-impression clarity: The home/login screen and the name “SafeEATS” alone weren’t descriptive enough; we recommended tighter value messaging on entry.
We also outlined in-flow feedback prompts (post-search rating à la Uber) to measure perceived confidence and experience quality.

Prototype testing revealed two key opportunities:

“Browse” ambiguity: 2 of 4 testers confused the post-filter browse view with the global nearby browse. We flagged copy/labeling and navigation refinements.
First-impression clarity: The home/login screen and the name “SafeEATS” alone weren’t descriptive enough; we recommended tighter value messaging on entry.
We also outlined in-flow feedback prompts (post-search rating à la Uber) to measure perceived confidence and experience quality.

Research Feedback & Design Learnings

Speed over scroll

Inline multi-selects outperformed dropdowns for setting allergens/diets; users could add/switch/remove with less cognitive load.

Hierarchy Matters

Larger tap targets, rounded buttons, and added whitespace improved scannability; the clearer end-of-setup CTA reduced hesitancy.

Naming & Navigation Clarity

Naming & Nav Clarity

Naming & Navigation Clarity

Distinct labels for Filtered Results vs. Nearby Browse; reinforce the product’s promise above the fold on entry.

Segment coverage

Designing for religious/cultural and dietary restrictions required secondary research + interviews to ensure inclusive filters and feasible data models.

Solutions

Location-Based Search

Find restaurants near you that match your constraints—cutting research time and enabling spontaneous dining with confidence.

Advanced Filtering

Allergens, Diet, Culture/Religion: Set precise rules (e.g., peanut-free, gluten-free, halal, vegan). The system narrows options to what’s actually safe for you. (The team explored ML/AI-driven filtering to scale relevance)

Clear Results & Favorites

Results emphasize what’s safe and why; users can Favorite for quick recall—reducing repeat research.

Two Browsing Modes (Disambiguated)

Two Browsing Modes

Two Browsing Modes (Disambiguated)

Filtered Browse: Only restaurants matching your set restrictions.
Nearby Browse: Awareness of all restaurants nearby, clearly labeled so expectations are set.

Filtered Browse: Only restaurants matching your set restrictions.
Nearby Browse: Awareness of all restaurants nearby, clearly labeled so expectations are set.

Filtered Browse: Only restaurants matching your set restrictions.
Nearby Browse: Awareness of all restaurants nearby, clearly labeled so expectations are set.

Location-Based Search
Find restaurants near you that match your constraints–cutting research time and enabling dining with confidence.
Advanced Filtering
The system narrows options to the your preferences—price range and cuisine—as well as what’s actually safe.
Advanced Filtering
The system narrows options to the your preferences—price range and cuisine—as well as what’s actually safe.
Clear Results & Favorites
Results emphasize what’s safe and why; users can Favorite for quick recall—reducing repeat research.
Location-Based Search
Find restaurants near you that match your constraints–cutting research time and enabling dining with confidence.
Advanced Filtering
The system narrows options to the your preferences—price range and cuisine—as well as what’s actually safe.
Advanced Filtering
The system narrows options to the your preferences—price range and cuisine—as well as what’s actually safe.
Clear Results & Favorites
Results emphasize what’s safe and why; users can Favorite for quick recall—reducing repeat research.
Location-Based Search
Find restaurants near you that match your constraints–cutting research time and enabling dining with confidence.
Advanced Filtering
The system narrows options to the your preferences—price range and cuisine—as well as what’s actually safe.
Advanced Filtering
The system narrows options to the your preferences—price range and cuisine—as well as what’s actually safe.
Clear Results & Favorites
Results emphasize what’s safe and why; users can Favorite for quick recall—reducing repeat research.
Location-Based Search
Find restaurants near you that match your constraints–cutting research time and enabling dining with confidence.
Advanced Filtering
The system narrows options to the your preferences—price range and cuisine—as well as what’s actually safe.
Advanced Filtering
The system narrows options to the your preferences—price range and cuisine—as well as what’s actually safe.
Clear Results & Favorites
Results emphasize what’s safe and why; users can Favorite for quick recall—reducing repeat research.

Location-Based Search
Find restaurants near you that match your constraints–cutting research time and enabling dining with confidence.
Advanced Filtering
The system narrows options to the your preferences—price range and cuisine—as well as what’s actually safe.
Advanced Filtering
The system narrows options to the your preferences—price range and cuisine—as well as what’s actually safe.
Clear Results & Favorites
Results emphasize what’s safe and why; users can Favorite for quick recall—reducing repeat research.
Location-Based Search
Find restaurants near you that match your constraints–cutting research time and enabling dining with confidence.
Advanced Filtering
The system narrows options to the your preferences—price range and cuisine—as well as what’s actually safe.
Advanced Filtering
The system narrows options to the your preferences—price range and cuisine—as well as what’s actually safe.
Clear Results & Favorites
Results emphasize what’s safe and why; users can Favorite for quick recall—reducing repeat research.
Location-Based Search
Find restaurants near you that match your constraints–cutting research time and enabling dining with confidence.
Advanced Filtering
The system narrows options to the your preferences—price range and cuisine—as well as what’s actually safe.
Advanced Filtering
The system narrows options to the your preferences—price range and cuisine—as well as what’s actually safe.
Clear Results & Favorites
Results emphasize what’s safe and why; users can Favorite for quick recall—reducing repeat research.
Location-Based Search
Find restaurants near you that match your constraints–cutting research time and enabling dining with confidence.
Advanced Filtering
The system narrows options to the your preferences—price range and cuisine—as well as what’s actually safe.
Advanced Filtering
The system narrows options to the your preferences—price range and cuisine—as well as what’s actually safe.
Clear Results & Favorites
Results emphasize what’s safe and why; users can Favorite for quick recall—reducing repeat research.

Location-Based Search
Find restaurants near you that match your constraints–cutting research time and enabling dining with confidence.
Advanced Filtering
The system narrows options to the your preferences—price range and cuisine—as well as what’s actually safe.
Advanced Filtering
The system narrows options to the your preferences—price range and cuisine—as well as what’s actually safe.
Clear Results & Favorites
Results emphasize what’s safe and why; users can Favorite for quick recall—reducing repeat research.
Location-Based Search
Find restaurants near you that match your constraints–cutting research time and enabling dining with confidence.
Advanced Filtering
The system narrows options to the your preferences—price range and cuisine—as well as what’s actually safe.
Advanced Filtering
The system narrows options to the your preferences—price range and cuisine—as well as what’s actually safe.
Clear Results & Favorites
Results emphasize what’s safe and why; users can Favorite for quick recall—reducing repeat research.
Location-Based Search
Find restaurants near you that match your constraints–cutting research time and enabling dining with confidence.
Advanced Filtering
The system narrows options to the your preferences—price range and cuisine—as well as what’s actually safe.
Advanced Filtering
The system narrows options to the your preferences—price range and cuisine—as well as what’s actually safe.
Clear Results & Favorites
Results emphasize what’s safe and why; users can Favorite for quick recall—reducing repeat research.
Location-Based Search
Find restaurants near you that match your constraints–cutting research time and enabling dining with confidence.
Advanced Filtering
The system narrows options to the your preferences—price range and cuisine—as well as what’s actually safe.
Advanced Filtering
The system narrows options to the your preferences—price range and cuisine—as well as what’s actually safe.
Clear Results & Favorites
Results emphasize what’s safe and why; users can Favorite for quick recall—reducing repeat research.

Results

Shipped an MVP prototype with core flows implemented in React Native + Firebase, plus hi-fi designs in Figma and a public repo/spec.
Validated problem/solution fit with 13 participants, translating pain points into a focused, testable feature set.
Defined success metrics to track impact post-launch: target 75% of users report feeling more informed and confident when dining out; 90% report a positive dining experience using SafeEATS. (Measurement to be embedded in the user flow.)
Go-to-market groundwork: Prioritize chain restaurants first (menu stability, multi-state scale), then expand to local venues; explore partnerships with restaurants and platforms like UberEats/DoorDash based on generated leads and insights.

Shipped an MVP prototype with core flows implemented in React Native + Firebase, plus hi-fi designs in Figma and a public repo/spec.
Validated problem/solution fit with 13 participants, translating pain points into a focused, testable feature set.
Defined success metrics to track impact post-launch: target 75% of users report feeling more informed and confident when dining out; 90% report a positive dining experience using SafeEATS. (Measurement to be embedded in the user flow.)
Go-to-market groundwork: Prioritize chain restaurants first (menu stability, multi-state scale), then expand to local venues; explore partnerships with restaurants and platforms like UberEats/DoorDash based on generated leads and insights.