Mevora
- Active development
- Pre-launch
A Flutter + Firebase dating app that matches people on explainable signals, with safety and location privacy enforced on the server.
- Problem
- Most dating apps rank people with opaque scores and expose more personal data than they need to. Mevora explains why two people match, keeps exact location private and moves every trust-critical rule off the client.
- My contribution
- As the sole developer I design and build the Flutter app, the TypeScript Cloud Functions backend, the Firestore and Storage security rules, and the companion ASP.NET Core support and admin sites.
Engineering highlights
- Explainable compatibility Profile, music (tracks 40% · artists 30% · genres 20% · recent 10%) and relationship-question scores are computed on the server. No "AI matching" claims.
- Server-authoritative matching The discovery feed, swipes and mutual matches are produced by Cloud Functions, with radius, mutual-preference, activity and block filters applied server-side.
- Privacy and safety Exact GPS is readable only by its owner; everyone else sees a distance label. Reporting, blocking, message rate limits, a photo moderation pipeline and a 1:1 face check against a liveness selfie.
- End-to-end encrypted chat Text, image, GIF and voice messages are encrypted with X25519 + AES-GCM; Firestore and Storage only ever hold ciphertext.
- Monetization and compliance Boost in-app purchases with server-side receipt verification and refund handling; account deletion and personal data export.
- Tests and CI 280+ Flutter test files, 120+ Cloud Functions tests and emulator-based security-rules tests run on GitHub Actions.
Stack
- Flutter
- Dart
- Firebase Auth
- Cloud Firestore
- Cloud Functions · TypeScript
- FCM
- Spotify Web API
- ASP.NET Core
- GitHub Actions