Personal relevance
We prioritize what a specific user tends to enjoy over generic popularity or one-size-fits-all rankings.
JunicLab is a founder-led product studio based in South Korea. We build products that become more useful through personal preference and real-world behavior, rather than generic popularity alone.
Generic 4.5-star ratings don't answer the real question: "Where do people with tastes similar to mine actually enjoy eating?" We are building a service that matches you with places based on taste similarity and genuine revisit intent, starting in Daegu.
We keep restaurant taste profiles and restaurant preference signals separate. Recommendations are centered on the relationship between users with similar tastes and the restaurant preferences and behaviors observed from those users.
We prioritize what a specific user tends to enjoy over generic popularity or one-size-fits-all rankings.
We give more weight to preference signals such as revisit intent and restaurant interactions than to aggregate ratings alone.
External data provides the cold-start foundation. Long-term value comes from preference and behavior evidence generated inside the service as real usage grows.
JunicLab is preparing its first production service for a focused local closed beta.