AI Voice Assistant
A real-time AI voice assistant for restaurants built using Twilio and Retell to automate phone orders and customer interactions.
Project Overview
The Restaurant Voice Assistant is an AI-powered phone system designed to handle inbound calls for restaurants, enabling customers to place orders, ask questions, and interact naturally using voice. The system leverages Twilio for telephony and Retell for real-time conversational AI, creating a seamless voice experience that mimics human interaction. The goal of the project was to reduce operational overhead, improve customer experience, and automate repetitive phone interactions such as order taking, menu inquiries, and business hours requests. This project demonstrates my ability to design real-time AI systems, integrate telephony infrastructure, and build conversational workflows that operate in production environments.
My Role
I designed and implemented the full voice assistant system, including Twilio call handling, Retell conversational AI integration, dialogue flow design, prompt engineering, and backend logic for managing restaurant interactions. The project required combining telephony APIs, real-time AI processing, and conversational UX design to deliver a responsive and reliable voice experience.
The Problem Context
Restaurants receive a high volume of phone calls for orders and inquiries, which can overwhelm staff and lead to missed calls or inconsistent customer experiences.
- Staff are interrupted frequently by phone calls during peak hours.
- Missed calls result in lost revenue opportunities.
- Manual order taking is time-consuming and error-prone.
- Customers expect quick responses but often face wait times.
The objective was to build an AI voice assistant capable of handling restaurant calls in real time, improving efficiency while maintaining a natural customer experience.
Key Features & Workflow
AI Call Handling
Automatically answers inbound calls and engages customers in natural conversations.
Voice-Based Ordering
Allows customers to place orders through voice interaction with structured understanding.
Automated Customer Support
Handles common questions such as menu details, hours, and location.
Real-Time Processing
Uses low-latency AI responses to maintain a smooth, human-like conversation flow.
Technical Architecture
How the system components fit together.
Twilio Telephony Layer
Manages inbound calls, routing, and audio streaming between the caller and the AI system.
Retell Conversational Engine
Processes speech-to-text, generates responses, and converts them back to speech in real time.
Conversation & Order Logic
Handles dialogue flows, order structuring, and validation of user inputs.
Design Decisions
A key design focus was achieving a natural, low-latency voice experience that feels conversational rather than robotic.
- Incoming call is received via Twilio.
- Audio is streamed to the Retell AI engine.
- User intent is interpreted and processed.
- The system generates a response and continues the conversation flow.
This architecture enables real-time conversations that can handle ordering and customer support without human intervention.
Future Roadmap
- Integration with POS systems for automated order submission.
- Multi-language support for diverse customer bases.
- Advanced intent recognition and personalization.
- Analytics and call performance tracking.
- Voice biometrics for returning customers.
Technology Stack
Core technologies used to build this project.
Capabilities Demonstrated
Key areas of expertise highlighted by this project.
