Building a Modern SaaS Platform: Key Technical Decisions and Architecture

When building a modern SaaS platform, making the right technical decisions is crucial for long-term success. This article shares our experience in building an AI-Enhanced Interview Practice Platform, focusing on key technical decisions, architecture considerations, and implementation strategies.

Key Technical Decisions

1. Storage Solutions

Database Selection: MongoDB

We chose MongoDB for our primary database for several reasons:

Flexible document model suitable for unstructured interview records
Easy to scale and query
Excellent TypeScript support
Wide adoption in tech companies

Here's our basic data model:

// User collection type
interface User {
  _id: ObjectId;
  firebaseUID: string;
  profile: {
    name: string;
    email: string;
    createdAt: Date;
  };
  settings: {
    language: string;
    notifications: boolean;
  };
}
 
// Interview record type
interface InterviewRecord {
  _id: ObjectId;
  userId: ObjectId;
  type: 'ai' | 'peer';
  startTime: Date;
  endTime: Date;
  transcript: {
    text: string;
    timestamp: Date;
    speaker: string;
  }[];
  feedback: {
    points: string[];
    score: number;
    suggestions: string[];
  };
}

Media Storage: Google Cloud Storage

For media storage, we opted for Google Cloud Storage:

Seamless integration with other Google services
Support for streaming upload/download
Lifecycle management capabilities
Fine-grained access control

2. Authentication System

We implemented authentication using Firebase Authentication:

Generous free tier suitable for personal projects
Quick implementation
Comprehensive authentication features
Allows focus on core business logic

Integration with MongoDB:

// User service
class UserService {
  async createUser(firebaseUser: FirebaseUser) {
    const user: User = {
      firebaseUID: firebaseUser.uid,
      profile: {
        name: firebaseUser.displayName || '',
        email: firebaseUser.email || '',
        createdAt: new Date()
      },
      settings: {
        language: 'en',
        notifications: true
      }
    };
    
    return await this.userModel.create(user);
  }
 
  async findByFirebaseUID(firebaseUID: string) {
    return await this.userModel.findOne({ firebaseUID });
  }
}

3. Real-time Communication

Our real-time communication stack consists of three main technologies:

WebRTC for video conferencing:

class VideoService {
  async initializePeerConnection() {
    const configuration: RTCConfiguration = {
      iceServers: [
        { urls: 'stun:stun.l.google.com:19302' },
        {
          urls: 'turn:your-turn-server.com',
          username: process.env.TURN_USERNAME,
          credential: process.env.TURN_CREDENTIAL
        }
      ]
    };
    
    const peerConnection = new RTCPeerConnection(configuration);
    return peerConnection;
  }
 
  async startVideoStream() {
    const stream = await navigator.mediaDevices.getUserMedia({
      video: true,
      audio: true
    });
    return stream;
  }
}

WebSocket for real-time data:

// Server-side WebSocket handler
class WebSocketHandler {
  @SubscribeMessage('transcript')
  handleTranscript(client: Socket, payload: TranscriptData) {
    // Process real-time transcript
    this.transcriptService.process(payload);
    
    // Broadcast to relevant clients
    this.server.to(payload.roomId).emit('transcript', {
      text: payload.text,
      timestamp: new Date(),
      speaker: payload.speaker
    });
  }
}

Socket.IO for bidirectional communication:

// Client-side Socket.IO setup
class SocketService {
  private socket: Socket;
 
  constructor() {
    this.socket = io(process.env.SOCKET_SERVER_URL, {
      auth: {
        token: this.authService.getToken()
      },
      transports: ['websocket']
    });
 
    this.setupListeners();
  }
 
  private setupListeners() {
    this.socket.on('connect', () => {
      console.log('Connected to socket server');
    });
 
    this.socket.on('transcript', (data: TranscriptData) => {
      this.transcriptStore.update(data);
    });
  }
}

4. Deployment Strategy

We chose Google Cloud Platform (GCP) for deployment:

Integration with other Google services (Firebase Auth, Speech-to-Text)
Complete cloud service ecosystem
Professional-grade infrastructure

Our deployment architecture:

                   [Load Balancer]
                         |
        +----------------+-----------------+
        |                |                |
  [Web Server]    [API Server]    [Socket Server]
        |                |                |
        +----------------+-----------------+
                         |
                   [MongoDB Atlas]
                         |
              [Google Cloud Storage]

Implementation Roadmap

Phase 1: Foundation (2 weeks)

Project initialization
- Frontend (React + TypeScript + Vite)
- Backend (NestJS)
- AI Service (Python FastAPI)
- Docker development environment
Data storage setup
- MongoDB Atlas configuration
- Basic data models
- CRUD APIs
- Data validation

Phase 2: Core Features (3 weeks)

Voice services integration
Real-time communication
Interview assistance features
AI functionality
Video system implementation

Phase 3: Deployment & Optimization (2 weeks)

CI/CD configuration
Cloud platform deployment
Load balancing setup
Monitoring system

Development Tips

Technical Preparation
- Study WebRTC fundamentals
- Familiarize with Google Cloud services
- Understand microservices patterns
Development Approach
- Build MVP first
- Add features incrementally
- Regular code reviews
- Keep documentation updated
Testing Strategy
- Unit tests coverage
- Integration testing
- End-to-end testing
- Performance testing

Conclusion

Building a modern SaaS platform requires careful consideration of various technical aspects. Our choices in storage solutions, authentication systems, real-time communication, and deployment strategies were driven by both technical requirements and business needs.

The combination of MongoDB, Firebase Authentication, and Google Cloud Platform provides a solid foundation for building scalable applications. The integration of WebRTC and WebSocket enables real-time features that enhance user experience.

Remember that technical decisions should align with your project's specific needs and constraints. Regular evaluation and adjustment of these choices ensure the platform's continued success and growth.