Multi-tenant conversational AI platform for customer service automation
Multi-tenant conversational AI platform for customer service automation
Syntera is a portfolio project demonstrating production-quality implementation of a multi-tenant SaaS platform for AI-powered customer service. It handles real-time conversations across multiple channels (chat, voice, email) with context-aware responses, workflow automation, and analytics.
Portfolio Focus: This project showcases scalable architecture, real-time communication patterns, RAG implementations, and enterprise-grade security practices.
Key Features:
- Multi-channel AI conversations with intelligent routing
- Enterprise knowledge bases with RAG for accurate responses
- Workflow automation with custom triggers and actions
- Real-time analytics and performance monitoring
- Multi-tenant architecture with data isolation
| Category | Capabilities |
|---|---|
| Conversations | Multi-channel (chat, voice, email), context-aware, intelligent routing |
| AI Engine | GPT-4 with custom prompts, RAG knowledge bases, workflow automation |
| Enterprise | Multi-tenant SaaS, row-level security, production monitoring |
| Business | CRM integration, analytics dashboard, API-first design |
graph TB
subgraph "Client Layer"
WEB[Web Dashboard]
WIDGET[Embeddable Widget]
end
subgraph "API Gateway"
NEXT[Next.js Frontend]
end
subgraph "Microservices"
AGENT[Agent Service<br/>AI Orchestration]
CHAT[Chat Service<br/>Real-time Messaging]
KB[Knowledge Base<br/>RAG Processing]
VOICE[Voice Agent<br/>LiveKit Sessions]
end
subgraph "Data Layer"
PG[(PostgreSQL<br/>Business Data)]
MONGO[(MongoDB<br/>Conversations)]
REDIS[(Redis<br/>Cache/Sessions)]
end
subgraph "AI & External"
OPENAI[OpenAI GPT-4]
PINECONE[Pinecone Vectors]
LIVEKIT[LiveKit WebRTC]
end
WEB --> NEXT
WIDGET --> NEXT
NEXT --> AGENT
NEXT --> CHAT
NEXT --> KB
NEXT --> VOICE
AGENT --> OPENAI
KB --> PINECONE
VOICE --> LIVEKIT
AGENT --> PG
CHAT --> MONGO
CHAT --> REDIS
KB --> PG
- TypeScript: Type safety across the entire stack
- Microservices: Independent scaling for different workloads
- Dual Database: PostgreSQL for business data, MongoDB for conversations
- LiveKit: WebRTC for low-latency voice interactions
- Pinecone: Vector search for RAG knowledge retrieval
- Multi-tenancy: Row-level security with company isolation
- Error Handling: Sentry monitoring across all services
- Security: JWT auth, rate limiting, input validation
- Scalability: Horizontal scaling with Railway infrastructure
git clone https://github.com/StephaneWamba/syntera.git
cd syntera
# Configure environment variables
# Create .env.local with required API keys (see docs/DEPLOYMENT.md)
# Required: Supabase, OpenAI, LiveKit, Pinecone
# Install dependencies and run all services
pnpm install
pnpm run dev:allTry the platform: https://syntera-tau.vercel.app/
- Interactive chat with AI agents
- Voice conversations with LiveKit
- Real-time response generation
- Multi-language support
| Document | Purpose |
|---|---|
| 📖 Architecture | System design and data flow |
| 🔒 Security | Security measures and compliance |
| 🚀 Deployment | Production setup guide |
| 🔧 API Reference | Complete API documentation |
| ⚙️ Workflows | Automation and triggers |
- Cost Reduction: Automated routing can reduce support tickets by 60-80%
- Revenue Growth: AI-driven lead qualification and sales automation
- Scale: Designed to handle thousands of concurrent conversations
- Quality: 24/7 consistent, accurate responses
| Layer | Technologies |
|---|---|
| Frontend | Next.js 16, React 18, Tailwind CSS, Shadcn/ui |
| Backend | Node.js, Express, TypeScript, Socket.io |
| AI | OpenAI GPT-4, Pinecone, LiveKit Agents |
| Database | PostgreSQL, MongoDB, Redis |
| Infrastructure | Railway, Vercel, Docker |
Licensed under MIT License
Production-quality conversational AI platform demonstrating scalable architecture and enterprise patterns.