VeraVoice

AI-Powered Telephony Platform

General Overview

Telephonic AI V3 is a modular platform that automates and enhances every step of the telephony experience:

  • AI-Powered Call Handling: Real-time, AI-driven conversations for inbound and outbound calls.
  • Admin Dashboard: Modern, user-friendly dashboards for managing agents, calls, analytics, and more.
  • Integrated Dialer: Web-based dialer for seamless outbound and inbound call management.
  • Comprehensive Logging & Monitoring: Centralized log viewer and observability dashboards.
  • Analytics & Reporting: Actionable insights from calls, agent performance, and system health.

Use Cases: - Automating customer support and sales calls - Real-time call transcription and analysis - Agent onboarding and training - Monitoring and improving call quality and compliance

Tech Stack

Frontend: - Next.js (React, TypeScript): Modern admin dashboard and user interfaces - Tailwind CSS, Shadcn-ui: Fast, beautiful, and customizable UI components - Zustand, Zod, Auth.js: State management, validation, and authentication

Backend: - NestJS (TypeScript): Robust REST API, business logic, and integrations - MikroORM (PostgreSQL): Scalable, relational data storage

AI/ML: - Python (FastAPI, Uvicorn): AI backend for real-time processing - OpenAI GPT-4o (and related models): Natural language understanding, conversation, and transcription

Telephony: - Twilio: Inbound/outbound call routing and management

Infrastructure & DevOps: - Docker, Docker Compose: Containerized deployment - Grafana, Loki, Promtail: Log aggregation, monitoring, and visualization

How AI Powers the System

AI is the heart of Telephonic AI V3. Here’s how it works:

  • Real-Time Conversation: The AI backend connects directly to OpenAI’s GPT-4o via WebSocket, enabling live, dynamic conversations with callers.
  • Speech-to-Text & Text-to-Speech: Handles both audio and text input, transcribes calls, and generates natural-sounding responses.
  • Scenario Management: Dynamically adjusts prompts and conversation flows based on business logic, agent settings, and user input.
  • Session Management: Manages timeouts, end-call scenarios, and error handling (including rate limits and retries).
  • Integration: The AI backend acts as a bridge between Twilio (telephony) and OpenAI (AI), orchestrating the entire call experience.

AI Challenges & Solutions: - Latency: Optimized WebSocket handling and prompt management for real-time performance. - Error Handling: Robust retry logic and validation for OpenAI responses. - Customization: Modular prompt and scenario management for different business needs.

Technical Breakdown

Database & Storage: - PostgreSQL via MikroORM for structured data (users, agents, calls, analytics). - Session and call data managed in-memory and persisted as needed.

Backend Architecture: - NestJS microservices structure, with modules for users, agents, analytics, permissions, notifications, and more. - RESTful APIs for frontend and service integrations. - Swagger/OpenAPI for API documentation and testing.

AI Backend: - FastAPI app with modular routes for agent onboarding, Twilio integration, and log viewing. - OpenAI integration via async WebSocket for real-time AI. - Utility modules for logging, error handling, and prompt management.

Key API Routes: - /ai/session (WebSocket): Real-time AI session for calls - /ai/socket/incoming-call: Handles Twilio inbound calls - /ai/socket/outgoing-call: Handles Twilio outbound calls

Engineering Decisions: - Separation of concerns: Isolated AI, backend, frontend, and observability services. - Containerization: Docker for consistent, scalable deployment. - Observability: Grafana stack for logs and monitoring.

User Journey Walkthrough

Step 1: User Initiates a Call - The user (customer or agent) dials in via the web dialer or phone. - The frontend triggers a call event, routed through Twilio.

Step 2: Backend Orchestration - The backend receives the call event, authenticates the user, and determines the appropriate agent or AI flow. - Relevant session and agent data are fetched from the database.

Step 3: AI Session Begins - The backend signals the AI backend to start a real-time session. - The AI backend establishes a WebSocket connection with OpenAI, sets up prompts, and manages session state.

Step 4: Real-Time Conversation - The user speaks or types; audio/text is streamed to the AI backend. - The AI backend transcribes audio, processes input, and generates responses using GPT-4o. - Responses are sent back to the user in real time (via Twilio or the web dialer).

Step 5: Analytics & Logging - All interactions are logged and analyzed. - The admin dashboard displays real-time analytics, call transcripts, and agent performance.

Step 6: Monitoring & Observability - Logs from all services are aggregated and visualized in Grafana. - Admins can view system health, errors, and call logs in real time.

Text-Based Flowchart / Architecture

User (Web Dialer/Phone)
    ↓
Frontend (Next.js Dashboard)
    ↓
Backend (NestJS API)
    → Authenticates user
    → Fetches agent/session data
    ↓
Twilio (Telephony)
    ↓
AI Backend (FastAPI, Python)
    → Receives call event
    → Sets up session, prompts, and voice model
    → Establishes WebSocket with OpenAI
    ↔ Streams audio/text to OpenAI GPT-4o
    ↔ Receives AI responses (text/audio)
    ↓
Twilio / Frontend
    → Delivers AI response to user in real time
    ↓
Backend
    → Logs call data, updates analytics
    ↓
Database (PostgreSQL)
    → Stores user, agent, call, and analytics data
    ↓
Log Aggregation (Promtail → Loki)
    ↓
Grafana
    → Visualizes logs, analytics, and system health

Conclusion

Telephonic AI V3 is a showcase of what’s possible when cutting-edge AI meets robust engineering. By blending real-time AI, seamless telephony, and actionable analytics, we’ve created a platform that empowers businesses to automate, analyze, and elevate every customer interaction.

This project highlights our team’s expertise in AI integration, scalable backend design, and modern frontend development. Looking ahead, we plan to expand AI capabilities, add more analytics, and open up new integration possibilities—helping clients stay ahead in the age of intelligent automation.