Beli's relative ranking moat

Beli is maybe my favorite app on my phone, but if you're not familiar, it's a restaurant app that became popular when we all basically hated restaurant apps. There was this massive graveyard of startups in the space that seemed unfunnable for a few reasons. Like if they were monetizing with ads, their recommendations became suspicious. There was this extreme bias where only people who really hated or really loved a restaurant with the ones ranking. And then finally, it didn't actually solve the discovery problem. I heard this on a podcast a while ago from a VC. People wanted new places to go to, but there's only a fine tune number of restaurants in the world and so people didn't need new recommendations, they needed better recommendations that were tailored to them. And Bella actually solved this. So Bella was founded in 2021 with gamified discovery and categorization of food restaurants. With the basic belief that taste is very personal. The traditional star rating system is kind of opaque because it's hard for algorithms to differentiate between restaurants that are all ranked within a specific band. Like how do you know whether a 4.1 versus a 4.0 restaurant is better? Also how do you know how to recommend this to an individual? Instead, Bella used a relative ranking system where with a few simple questions, it could tell how much you liked a restaurant relative to others. This helped to create personalized taste profiles that they could then use to provide recommendations who had similar taste profiles. First part was that this was a private journal. I personally hate you because I hate that every single thing you write or use on it is public information. For anyone building consumer applications in spaces that are quite crowded. Bella is one of the best examples of Y-Drive data modes. Let me know what the hardest cut was that you had to make on a Balle comparison or a restaurant or coffee shop that I should check out.