Voice Calling Agents
Built autonomous voice AI agents that handle 1,000+ calls daily for US logistics companies, automating lead qualification and appointment booking with sub-500ms response latency.
1,000+
Daily Calls Handled
<500ms
Response Latency
15+
Companies Served
73%
Cost Reduction
The Challenge
US logistics companies were drowning in manual phone operations. Sales teams spent 70% of their time on repetitive calls—qualifying leads, booking appointments, and following up. The human bottleneck was costing companies millions in missed opportunities and operational inefficiency.
Key Challenges
- •High volume of repetitive calls consuming valuable sales team time
- •Inconsistent lead qualification leading to poor conversion rates
- •24/7 availability requirements impossible to meet with human agents
- •Language barriers and accent variations in a diverse customer base
- •Need for real-time CRM integration and appointment scheduling
The Solution
I architected and built a fully autonomous voice AI system that handles the entire call lifecycle—from initial contact to appointment confirmation. The system uses a novel multi-agent architecture where specialized agents handle different conversation phases, ensuring natural dialogue flow and high task completion rates.
Designed a multi-agent orchestration layer using LangGraph for complex conversation state management
Implemented real-time speech-to-text with Deepgram's Nova-2 model for 98%+ transcription accuracy
Built custom voice synthesis pipeline with ElevenLabs for natural, brand-consistent voice output
Created dynamic prompt engineering system that adapts to conversation context in real-time
Integrated with existing CRM systems via FastAPI webhooks for seamless data synchronization
System Architecture
The system follows a microservices architecture with event-driven communication. Each component is designed for horizontal scalability and fault tolerance.
Voice Gateway
Handles telephony integration via Twilio, managing call lifecycle events and audio streaming with WebSocket connections for real-time bidirectional audio.
Speech Processing Pipeline
Deepgram Nova-2 for STT with custom vocabulary boosting for logistics terminology. ElevenLabs for TTS with voice cloning for brand consistency.
Conversation Engine
LangGraph-based state machine managing conversation flow. Supports interruption handling, context switching, and graceful error recovery.
Agent Orchestrator
Coordinates between specialized agents: Qualifier Agent, Scheduler Agent, and Objection Handler. Uses GPT-4 for complex reasoning.
Integration Layer
FastAPI services connecting to CRM (Salesforce, HubSpot), calendar systems (Google Calendar, Calendly), and internal databases.
Key Implementation Details
Ultra-Low Latency Pipeline
Achieving sub-500ms response time required aggressive optimization. I implemented streaming STT with partial transcript processing, allowing the LLM to begin generating responses before the user finishes speaking. Combined with chunked TTS streaming, this creates natural conversational flow.
Interruption Handling
Real conversations involve interruptions. I built a voice activity detection (VAD) system that monitors for user speech during agent responses, immediately halting TTS output and processing the interruption. This required careful audio buffer management and state reconciliation.
Dynamic Context Management
Each call maintains a rich context window including caller history, previous interactions, and real-time sentiment analysis. The context is pruned and summarized using a sliding window approach to stay within token limits while preserving critical information.
Graceful Degradation
When STT confidence drops below threshold or network issues occur, the system gracefully requests clarification or offers to transfer to a human agent. This maintains user trust and ensures no lead is lost due to technical issues.
Tech Stack
AI/ML
Backend
Telephony
Infrastructure
Key Learnings
Latency is everything in voice AI—users perceive delays over 600ms as unnatural
Edge cases in conversation (interruptions, background noise, accents) require dedicated handling
Real-time systems need comprehensive observability from day one
Voice AI requires different prompt engineering than text-based systems—brevity and clarity are paramount
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