Problem Statement
Many businesses still depend on human agents to handle repetitive phone conversations such as lead qualification, customer inquiries, follow-ups, and initial information gathering. These interactions can require significant time while following relatively structured conversation flows.
For businesses receiving or making calls at scale, the challenge is to create a voice system that can handle conversations naturally while still following predefined business logic, dynamically adapting to information provided by the caller, and passing the resulting data into existing business systems.
Proposed Solution
Production-ready AI voice agents were developed and deployed for lending and home-services clients, supporting both inbound and outbound conversations.
The agents combine speech recognition, conversational intelligence, voice synthesis, and workflow automation to create complete phone-based AI experiences.
Conversational Voice Layer
The systems use technologies including:
Retell AI · LiveKit · Deepgram · ElevenLabs
These components handle the underlying voice interaction, allowing the AI agent to listen, process, and respond during live telephone conversations.
Dynamic Call Flows
Rather than using a completely static script, the agents use dynamic variables to adapt the conversation based on information available during each call.
This allows the same underlying agent architecture to support different business contexts and conversation paths.
CRM & Workflow Integration
Call outcomes and collected information can be passed into external business systems through webhooks and Make.com, connecting the voice conversation to downstream workflows.
Voice Conversation → AI Agent → Call Outcome / Data → Webhook → CRM / Automation
Core Idea
Turn business phone conversations into intelligent, automated workflows.
Technology Stack
Retell AI · LiveKit · Deepgram · ElevenLabs · Make.com · Webhooks · Conversational AI · Speech AI
Outcome
The project demonstrates the deployment of production voice agents, rather than simply experimenting with a voice chatbot—connecting real-time conversations with the operational systems that businesses use after the call.