Cloud-Based AI Voice Agent Creation

Bid - if you have already created something with demo of the platform
I can divide the project into frontend and backend also if required.
AI Voice Agent Platform

1. Project Overview

Goal: Build a cloud-based platform that enables users to create, configure, and deploy AI voice agents capable of real-time voice conversations over phone calls or web calls.

The platform will integrate with LiveKit for real-time streaming, Plevo and Twilio for telephony, and allow customization of LLM models, transcribers, voices, and custom business logic functions.

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2. Core Components

2.1 Agent Builder Interface

A web-based UI where users can create and configure their own AI voice agents.

Features:
• Agent Creation Flow
• Agent Name, Description
• System Prompt / Persona setup
• LLM Selection
• Options: e.g., OpenAI GPT models, Anthropic Claude, Mistral, local LLMs, etc.
• Configuration: Temperature, Max Tokens, API Keys (user-provided or system)
• Transcriber Selection
• Available transcribers: e.g., Whisper, Deepgram, AssemblyAI, Rev.ai, etc.
• Config options: language, accuracy vs latency, etc.
• Voice Model Selection
• Voice Providers: e.g., ElevenLabs, Play.ht, OpenAI TTS, Azure Voices
• For each model → multiple available voices (male/female styles, accents)
• Config options: speaking rate, pitch, tone

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2.2 Real-Time Communication Layer

LiveKit Integration
• Used for streaming audio between user and AI agent (low latency).
• Bi-directional audio streaming for real-time conversation.

Telephony Integration
• Plevo and Twilio support:
• Handle inbound/outbound call setup
• Manage session initiation and bridging with LiveKit
• Forward/transmit audio streams to/from LiveKit

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2.3 Custom Function Integrations

Allow users to attach custom callable functions that the AI can trigger mid-call.

Examples:
• Calendly Integration: Schedule meetings automatically
• Transfer Call: Forward to human agent or another number
• Webhooks: User can define external APIs to trigger (e.g., CRM updates, support tickets)
• Each custom function registered via webhook URL, accessible to the LLM via function calling or schema definition.

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2.4 Post-Call Analysis and Webhooks

After each call session:
• Generate Call Summary, Transcript, Key Insights, Sentiment, and Action Items.
• Send data to a Post-Call Webhook URL provided by the user.
• Display analytics in the user dashboard:
• Duration, sentiment trends, conversation quality
• Keyword highlights
• Conversion metrics (if applicable)

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2.5 Dashboard and Analytics

Key Views:
• Agent Overview: List of all agents, active/inactive status
• Call Logs: Recordings, transcripts, summaries
• Analytics:
• Call durations, engagement rates
• Sentiment and success metrics
• Function call statistics (e.g., how often Calendly or webhook triggered)
• Integration Settings:
• Manage LLM keys, Transcriber keys, Voice API keys
• Webhooks for Post-Call and Custom Functions

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3. Backend Infrastructure

Core Services:
• Agent Management Service: CRUD for agents and configurations
• Session Service: Handles LiveKit and Telephony orchestration
• LLM Gateway: Routes requests to chosen LLM provider
• Transcription Service: Connects to selected transcriber in real time
• Voice Synthesis Service: Streams AI’s spoken responses
• Webhook Service: Manages outgoing post-call and custom function calls
• Analytics Service: Processes and stores call summaries and metrics

Data Storage:
• PostgreSQL / MongoDB: for agent configs, call logs, and analytics
• Object Storage (S3): for call recordings and transcripts
• Redis / PubSub: for real-time state and streaming event sync

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4. Authentication and Access
• OAuth / API Key for platform access
• User-level integration credentials (e.g., Twilio, ElevenLabs)
• Role-based access control (admin, developer, analyst)

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5. Scalability and Deployment
• Microservices architecture using containerized services (Docker, Kubernetes)
• Scalable audio streaming through LiveKit servers
• Queue-based event processing for post-call analytics and webhooks
• Monitoring: Logging, performance metrics, failure alerts

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6. High-Level Tech Stack (Suggested)

Layer Tech Stack
Frontend React / Next.js
Backend Node.js / Python (FastAPI or NestJS)
Real-time LiveKit
Telephony Twilio, Plevo
LLM OpenAI, Anthropic, Mistral (pluggable)
Transcription Whisper, Deepgram, AssemblyAI
Voice ElevenLabs, Play.ht, Azure TTS
Database PostgreSQL / MongoDB
Hosting AWS / GCP / Azure
Storage S3-compatible
Monitoring Prometheus + Grafana / Datadog


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7. Example Agent Workflow
1. Incoming Call via Twilio/Plevo
→ Routed through LiveKit session
→ Audio streamed to transcriber
2. Transcription → LLM Processing
→ LLM generates response using context and custom functions
3. Voice Synthesis
→ Generated voice streamed back to caller in real time
4. Optional Custom Functions Triggered
→ Example: Book meeting via Calendly webhook
5. Call Ends → Analytics Generated
→ Post-call webhook triggered with summary and metrics

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