Background
OpenGram is a restaurant booking and community app that merges the efficiency of OpenTable with the social engagement of Instagram. Users can browse real, verified reviews, book restaurants, and join community conversations — all within one seamless experience.
I conducted secondary and primary research, translated insights into feature concepts, built wireframes and prototypes, and iteratively refined the design through user testing and optimization.
Core problem
Addressing Pain Points in Search, Decision-Making, and Community Connection:
Authenticity: Reviews are questionable. Users don’t trust reviews because many feel fake or outdated, while our goal is to provide only verified reviews based on real, completed visits.
Inconsistency: Restaurant booking is fragmented. Users switch between multiple apps to find, check, and book restaurants. Our design goal is to create a single, seamless flow from discovery → booking → reviewing.
Inclusivity: Community features exist but feel disconnected. Users struggle to find suggestions that match their culture, diet, or context.Goal: Support personalized filters that reflect real user identities and needs. The goal is to support personalized filters that reflect real user identities and needs.
16 Primary interviews helped us uncover the emotional, cultural, and contextual factors driving restaurant choices.
Secondary research helped us understand broad industry patterns
We recruited 16 people from student groups and Friends-of-friends network, reflecting the diverse types of users who struggle with fragmented dining journeys. Insight of navigating restaurant discovery in a new city from primary research:
Understand decision-making behaviors
Identify nuanced pain points
Validate assumptions about fragmented app usage
Capture culturally rooted trust factors
Surface unmet needs for future product strategy
Interview Findings Highlight Gaps in Trust, Authenticity, and Platform Cohesion Across the Dining Decision Flow
Fragmented discovery journey: users switch between Google Maps, Yelp, Instagram, and group chats to make one decision.
Low trust in reviews: preference for community-based, culturally familiar sources over sponsored or overly positive platforms.
Context-driven decisions: (choices vary by situation) casual meals, travel, special occasions, or budget vs. ambiance trade-offs.
Information overload: too many options lead to decision fatigue and slow decision-making.
Need for simplicity: strong desire for one intuitive, reliable tool that reduces cognitive load.
Results & Insights
Home Screen: Landmark-Based Search for Tourists
We introduced landmark-anchored search filters (e.g., “Near CN Tower,” “By Stanley Park,” “At Union Station”) so users can quickly find restaurants based on familiar points of reference, which aligns with how travelers think and dramatically reduces the effort of searching in unfamiliar areas.
We redesigned the information architecture to guide intention-driven exploration: user intent → narrowing options → comparing choices.
Users actively avoid biased recommendations and desire neutrality
Our design centers neutrality by minimizing visual interference
One Stop App Usage: One-Screen Comparison Map → Community → Albums → Chat → Shared Booking Link
We designed an integrated map comparison view where users can
Explore restaurants
Compare menus, reviews & price ranges
Current wait time
Seat availability
“Best for” tags (Date night, Kid-friendly, Quick lunch)
Review Interactions Made the Experience Feel More Social & Trustworthy
By shifting reviews from one-way posts to a community-driven system, we made restaurant discovery more personal, culturally relevant, and socially interactive, strengthening trust and reducing decision fatigue.
Community Feed: collective dining experiences
Identity Markers: identity tags help users filter content they relate to
Meaningful Interaction: earn credibility & highlight “Community Notes”
AI-Powered Summaries: reduces cognitive load and builds trust through transparency
