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Seed Round ($5M valuation, $1M raise) Cap Table: Joseph, Clifford, Max,…

- - - - Based on our calculations, it is a sufficient and reasonable amount to reach two objectives - 1M users + product market-fit In fact, we are doing seed at this quantum because we want a larger upside for all our stakeholders when we are ready to raise a much larger round at Series A.
      - Once the app is notable, we are in a unique geographical location in Singapore where there hasnt been a social app that sprung out before. We will be able to hold a lot of influence and set narratives for countries in our region.
      - We do want the right people on board because this is something that is powerful and therefore should fall in the right hands.
    - - With both of these in place, we will be able to head to Series A and raise a much larger round.
      - $1M will also help us increase our speed of iteration by allowing us to start hiring. Bugs and crashes will be spotted and fixed quicker, features will be rolled out faster, overall enhances the user experience, making them want to stay on the app.
      - $1M will help us to achieve PMF. We will be able to create an app experience that users cannot find elsewhere. We would also achieve word of mouth, and our viral coefficient will be > 1, which means that on average, every user that joins huddle will bring in another user.
- - - - Second, we'll be using AI to keep our users safe as long as they are on Huddle. AI moderation does just that by removing up to 99% of harmful content, whilst eliminating the need to hire 100s of moderators, saving us lots of money and time.
      - AI Articles.
        
        Third, we feel that sharing is an integral part of every community. We want to make knowledge easy to acquire so as to elevate the user experience on Huddle. Having our own wikipedia would never be discussed 5 years ago, but it's possible now. Wikipedia took 25 years to manually input 7 million articles but I reckon that we could do the same 10 times faster.
      - First we will be using AI to curate content that users truly love and push it out to them. This involves drawing and processing a myriad of information to create a deep profiling of each user.
  - - - Then, we will catalog a list of smart prompts to generate summarized wiki pages based on the queries of users
      - Next, these wiki pages will be automatically stored on our servers and be accessible to everyone.
      - We will establish an end-to-end point from our app to gpt-5
  - - - We gather the necessary information
      - We build the foundational data sets
      - We store the metadata that is required for machine learning
      - We feed those information into the AI model to craft out a uniquely customized AI algorithm for each individual user.
- - - - Thereafter we have things like spam, scam, and misbehaviour. This is where the second layer - community moderation comes in. Every community in the app will be self-governed and moderated based on their own set of guidelines which are bound within our platform wide standards and global content policies. Here, local enforcement respond quicker and are more content-aware. Doing this helps us scale our users faster without worrying about centralising manpower on moderation. This is a proven method modelled over Wikipedia and Reddit.
      - The 3rd layer is manual review where it will be handled by our internal team. 99% of cases will be removed at this point, and what's left are mostly edge cases or escalation-worthy. We have designed this intentionally so that this layer will not overwhelm the team so that they can focus on what matters the most. 1 team member or a volunteer will be here to take care of these reports.
      - First and topmost layer is AI moderation. AI moderation will be deployed universally on our huddle and handle harmful content like gore, nudity, and violence before it even reaches our community members. This will address the most critical moderation challenge.
- - - - We believe communities thrive on apps because:
        it can be made easy navigate
        Convenient
        there are real time updates
        
        The core differentiation between us and them is the focus of communities. Their focus is on the participation of users in large communities. Whereas our focus is on solving the authenticity of communities itself. The gap we have noticed with their model is that personal identities and authentic connections are diminished when communities reach a certain size. Many users end up being spectators or lurkers. An incredible ton of potential engagement, virtual identity, and contribution are lost in the pursuit of establishing large communities. This is why we created group chats within communities to not only solve the overarching issue of authenticity, but to capitalize on multiplied activity, engagement, and contribution.