Dating App - ConviConnect

A next-generation dating app that matches users based on shared preferences, interests, and values, with a unique safety-first approach.

Project Overview

This is a next-generation dating app that matches users based on shared preferences, interests, values, lifestyle, offering a more personalized and meaningful connection experience. What truly sets it apart is its unique safety-first approach—the app recommends vetted dating locations such as cafes, restaurants, or public spaces that have been reviewed for safety and comfort. This helps ensure that first-time meetups happen in secure, trusted environments, reducing the risk of uncomfortable or dangerous situations. The app also integrates real-time check-ins and optional safety alerts, giving users and their loved ones peace of mind. Designed with modern dating challenges in mind, this platform prioritizes both compatibility and personal safety.

Mobile Application Showcase

Dating App Screen 1
Dating App Screen 2
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Dating App Screen 6
Dating App Screen 7
Dating App Screen 8
Dating App Screen 9

Key Features

  • Profile Setup: Users must complete their dating profile to access dating functionalities.
  • 1-on-1 Matching: Discover and connect with compatible users.
  • Match History: View records of previously matched users.
  • Community Events: Display local dating and social events within a 10km radius.
  • Heatmap: Visual representation of nearby dating and social activity.
  • AI Assistant Chat: AI personalizes interactions based on user profiles.

Solutions

  1. Required mandatory profile setup with verification before unlocking features.
  2. Used AI-driven compatibility algorithms for high-quality matching.
  3. Provided a streamlined interface to manage recent profiles and matches.
  4. Implemented match history logs with metadata on interaction outcomes.
  5. Automatically displayed local community events using geolocation filters.
  6. Used heatmaps to visually depict active users and event clusters.
  7. Integrated live chat modules with moderation and reporting features.
  8. Used user-fed preference modeling to train the AI assistant.

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