Syntera — AI Customer Service Platform (Portfolio Case Study)

Overview

Syntera is a production-grade portfolio project: a multi-tenant SaaS platform that combines voice AI, chat conversations, and workflow automation. Businesses can deploy AI customer service across voice and chat channels, manage knowledge bases, and automate workflows—all without building from scratch.

The problem this project targets

Businesses need conversational AI that works across channels (voice and chat), stays up-to-date with their knowledge base, and integrates with their existing tools. Building such systems from scratch is complex and time-consuming. Syntera provides a complete platform where businesses can deploy AI agents, manage knowledge, and automate workflows visually.

What I built

1) Multi-channel conversational AI

• Voice AI: Natural-sounding voice conversations with noise cancellation and multilingual support
• Chat conversations: Text-based AI conversations with the same knowledge base as voice
• Unified knowledge: Voice and chat share the same information, so customers get consistent answers across channels
• CRM integration: Voice and chat can create contacts, update deals, and trigger workflows

2) Visual workflow automation

• Drag-and-drop workflow builder for business users (no coding required)
• Workflows can trigger from conversation events, CRM updates, or webhooks
• Actions include sending emails, updating CRM deals, making HTTP requests, and more
• Conditional logic lets you build complex automation rules
• Reliable execution with error handling and history tracking

3) Real-time knowledge bases

• Upload documents and they become searchable within minutes (no model retraining needed)
• Knowledge bases update in real-time as you add or modify documents
• Frequently accessed information is cached for faster responses and lower costs
• Each company's knowledge base is completely isolated

4) Multi-tenant security

• Database-level security ensures each company's data is completely isolated
• Even if stored in the same database, companies cannot access each other's data
• Security is enforced at the database level, providing defense-in-depth protection

5) Supporting features

• Sentiment analysis: Real-time detection with sentiment-aware responses
• Built-in CRM: Contact management, sales pipeline, automatic extraction from conversations
• Analytics dashboard: Conversation metrics, performance tracking, cost analysis
• Embeddable widget: Add AI chat to your website with API key authentication
• Conversation transcripts: Searchable history of all conversations

Technical deep dive

How it works

The platform consists of independent services (chat, voice, knowledge base, workflows) that work together. When a customer interacts via voice or chat, the AI agent searches the company's knowledge base, generates responses, and can trigger workflows that integrate with external systems.

Technology stack

• Frontend: Next.js dashboard with optimized performance and real-time updates
• Backend: Microservices architecture (chat, voice, agent, knowledge base) that scale independently
• Voice: WebRTC-based voice conversations with multiple TTS providers
• AI: GPT-4 for conversation and analysis
• Search: Vector search for knowledge base retrieval
• Infrastructure: Production deployment with monitoring, error tracking, and database-level security

Key technical decisions

• Microservices architecture: Independent services enable independent scaling and deployment
• Real-time knowledge updates: Documents become searchable within minutes without model retraining
• Database-level security: Multi-tenant isolation enforced at the database level for maximum security
• Performance optimization: Code splitting and intelligent caching keep the dashboard fast and responsive