C-Lync

SaaS Manajemen & Otomatisasi WhatsApp Berbasis Google AI

🌐 Live Production

Why This · Features · Tech Stack · Architecture

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🕒 Why C-Lync?

🖼️ Preview

Web App Preview

Chat WhatsApp numpuk, pertanyaan yang sama berulang, respon lambat. Pemilik bisnis kecil keteteran balas chat manual seharian.

C-Lync hadir sebagai asisten cerdas yang menjembatani WhatsApp Web Gateway dengan infrastruktur kecerdasan buatan Google untuk mengotomatisasikan rangkuman obrolan dan pencatatan memori data kontak secara asinkron.

⚡ AI Chat Summarization	Rangkum puluhan pesan panjang jadi 1 baris info krusial instan
🔒 Dynamic Contact Memory	AI ekstrak preferensi kontak otomatis ke profil memori
🛡️ Multi-Tenant Security	Isolasi data 100% via Supabase RLS per-user

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🎯 Key Features

AI-Powered Capabilities

Feature	Status	Description
AI Automated Summarization	✅	Merangkum puluhan baris obrolan panjang menjadi satu baris informasi krusial instan
Dynamic Contact Memory	✅	Mengekstrak informasi preferensi unik dari pesan kontak secara otomatis
Gemini AI Integration	✅	Pemahaman konteks bahasa Indonesia yang unggul via Google AI Studio

Core Platform Features

Feature	Status	Description
Web-based Dashboard	✅	Akses dari browser, ga perlu install aplikasi tambahan
Multi-Conversation Handling	✅	Kelola banyak chat WhatsApp dari satu dashboard terpusat
Sub-Tab Isolation UI	✅	Pemisahan steril antara demo walkthrough dan data produksi
Realtime Badge Counter	✅	Counter unread badge bergerak dinamis via Supabase Realtime

Infrastructure

Feature	Status	Description
Google Cloud Run Deployment	✅	Berjalan di GCP dengan RAM hemat < 512MiB
Unified Monolith Architecture	✅	Frontend + Backend dalam satu port untuk minimalkan cold starts
PostgreSQL 17 + RLS 100%	✅	Isolasi data multi-tenant via Row Level Security

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🛠️ Tech Stack

Layer	Technology	Why
Frontend	React.js, TypeScript, Tailwind CSS, Framer Motion	Antarmuka taktil dengan touch targets >= 44px
Backend	Express.js, Node.js	Unified monolith, minimal cold starts
AI Engine	Google AI Studio (Gemini Pro SDK)	Kecepatan inferensi tinggi, konteks ID unggul
Database	Supabase (PostgreSQL 17.6)	RLS 100%, Realtime subscription
Deployment	Google Cloud Run	Scalable serverless container

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🏗️ Architecture

c-lync/
├── images/                  # Visual assets (banner, demo gif)
├── src/
│   ├── components/         # Modular UI components (BottomTabs, CustomDialog)
│   │   └── settings/       # Profile, billing, WA connection settings
│   ├── context/
│   │   └── AppContext.tsx  # Global state + onboarding flag
│   ├── jobs/               # Background jobs (autoCleanup, userDeletion)
│   ├── lib/
│   │   ├── apiClient.ts    # Centralized fetch wrapper
│   │   ├── geminiClient.ts # Google GenAI SDK integration
│   │   └── supabase.ts     # Database client
│   ├── middleware/
│   │   ├── auth.ts         # JWT authentication guard
│   │   └── tokenQuotaCheck.ts  # AI token quota limiter
│   ├── routes/              # Legal routes (GDPR, Privacy Policy)
│   ├── screens/
│   │   ├── AiAgents.tsx    # Gemini AI chat interaction
│   │   ├── AuthScreen.tsx   # Multi-tenant login/register
│   │   ├── Chats.tsx       # Core UI (Real vs Sandbox tabs)
│   │   ├── Dashboard.tsx    # Token analytics & quota panel
│   │   └── Settings.tsx     # Business profile controls
│   ├── services/
│   │   ├── baileyStateManager.ts  # WA multi-file auth sync
│   │   ├── memoryBackupService.ts
│   │   └── tokenQuotaService.ts   # Per-user AI quota cutter
│   └── utils/
│       ├── promptBuilder.ts       # Safe prompt constructor
│       └── promptOptimizer.ts     # Context token optimizer
├── supabase/
│   └── migrations/         # SQL schema migrations
├── server.ts               # Express core engine + static server
├── whatsappService.ts      # Baileys WA Socket integration
├── SUPABASE_SCHEMA.sql     # Complete RLS + table schema
└── package.json            # Dependencies

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⚙️ System Flow

[ User ]
  │
  ├── Login / Register (protected by Supabase RLS 100%)
  │
  ├── WhatsApp Web Sync (scan QR via Baileys multi-file auth)
  │
  └── Core Menu (Chats Screen)
        │
        ├── SANDBOX SIMULASI TAB
        │     └── Pure in-memory demo, bypass database quota
        │
        └── PESAN NYATA TAB
              └── PostgreSQL Realtime Listener
                    └── Badge counter updates dynamically

[ Background Circuit ]
  │
  ├── Pesan Masuk ──► Save to whatsapp_messages table
  │
  ├── Klik "SUMMARY" ──► promptOptimizer.ts compiles payload
  │
  ├── Inferensi ──► Google AI Studio processes summary
  │
  └── RAM Server stays < 512MiB

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🏆 Awards

Project Hasil Kompetisi #JuaraVibeCoding (Google Cloud Indonesia)

Catatan Evaluasi Juri: Berkas ini merupakan Mirror / Shadow Repository yang dirancang khusus untuk menyajikan dokumentasi arsitektur sistem, bagan alur kerja, serta cetak biru struktur folder C-Lync (Project Challenge 11/50) secara transparan. Kode sumber inti (Core Engine) disimpan aman di dalam repositori privat demi melindungi token API komersial dan hak cipta kekayaan intelektual.

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📦 License

This repository is a public documentation mirror for the C-Lync project.

• Core Code: Proprietary / Komersial Terbatas — disimpan di repositori privat
• Documentation: Mirror ini disediakan untuk transparansi arsitektur dan portfolio

For licensing inquiries, contact the author.

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🔗 Connect

_{Built with passion as the 11th Project of the 50 Projects Challenge by @curzyori}