You will get a real-time voice AI agent built with LiveKit
Project details
Real-time voice AI agents transform how businesses interact with customers, replacing hold queues and scripted IVR menus with natural, intelligent conversations. This project delivers a fully functional voice agent built on LiveKit, the leading open-source platform for real-time audio and video.
Your voice agent will handle speech-to-text using Deepgram, process conversations through an LLM of your choice (OpenAI or Claude), and respond with natural text-to-speech using Cartesia. The entire pipeline runs with sub-500ms latency for truly conversational interactions.
We recently built a voice AI assistant for a healthcare company serving Medicare patients, delivering a working MVP in 2 weeks with sub-500ms response latency. Our team consists of senior engineers only, each with 10+ years of experience. We write production-ready code from day one, not prototypes that need to be rewritten.
Your voice agent will handle speech-to-text using Deepgram, process conversations through an LLM of your choice (OpenAI or Claude), and respond with natural text-to-speech using Cartesia. The entire pipeline runs with sub-500ms latency for truly conversational interactions.
We recently built a voice AI assistant for a healthcare company serving Medicare patients, delivering a working MVP in 2 weeks with sub-500ms response latency. Our team consists of senior engineers only, each with 10+ years of experience. We write production-ready code from day one, not prototypes that need to be rewritten.
AI Algorithms
Large Language ModelAI Applications
AI Text-to-Speech, Automatic Speech Recognition, Conversational AIAI Development Language
PythonAI Models
GPT-4, WhisperWhat's included
| Service Tiers |
Starter
$6,000
|
Standard
$12,000
|
Advanced
$18,000
|
|---|---|---|---|
| Delivery Time | 14 days | 21 days | 35 days |
Number of Revisions | 1 | 2 | 3 |
AI Model Integration | |||
Batch Normalization | - | - | - |
Database Integration | - | - | - |
Detailed Code Comments | - | - | - |
Image Upscaling | - | - | - |
MLOps | - | - | - |
Model Deployment | - | ||
Model Documentation | - | - | |
Model Monitoring | - | - | - |
Model Testing & Optimization | - | ||
Model Tuning | - | - | - |
Natural Language Processing | |||
NLP Tokenization | - | - | - |
Pre-Training | - | - | - |
Prompt Engineering | |||
Setup File | - | - | - |
Source Code |
Frequently asked questions
About Carlos
Senior AI Engineer | LangChain, RAG, Multi-Agent Systems, Voice AI
Atibaia, Brazil - 3:34 am local time
Senior AI/ML Engineer with 15+ years of experience building production systems across generative AI, machine learning, and data science. Toptal developer since 2020, delivering projects for companies including Typeform, LotLinx, and Everyday AI.
At Typeform, I designed multi-agent system architecture using Amazon Bedrock and LangGraph, implementing supervisor-based orchestration with streaming inter-agent communication across specialized domain agents. I built evaluation frameworks combining deterministic validation, LLM-as-judge assessment, and API dry-run testing with MLflow integration.
At LotLinx, I contributed to LotGPT, the first dealer-facing conversational AI for car dealerships. I built RAG systems integrating sales, market, and vehicle data through vector databases, and led development of an AI vehicle image enhancement system using vision models and computer vision.
At Everyday AI, I developed a full-stack voice assistant using LiveKit, Next.js, and Python, delivering real-time conversational experiences for elderly care applications with sub-500ms voice latency.
Before Toptal, I served as Data Science Manager at Trustly, where I managed the gaming and crypto portfolio serving companies like Kraken, DraftKings, and FanDuel. I led deep learning and LLM initiatives for income prediction and bank activity categorization, and delivered the company's first LLM-powered chatbot.
At Wildlife Studios (4 years), I rose to Senior Staff Data Scientist and Senior Manager, supporting Sniper 3D in achieving a 20% revenue increase through experimentation, statistical modeling, and dynamic pricing. I built an AI assistant using LLMs to help business analysts perform analysis in Looker.
Core expertise: LangChain, LangGraph, RAG systems, multi-agent architectures, voice AI (LiveKit, Deepgram, Cartesia), Python, AWS (Lambda, Bedrock, SageMaker), OpenAI/Claude APIs, vector databases (Pinecone, Weaviate, Chroma), deep learning (PyTorch), and MLOps.
Education: Master's and Bachelor's in Applied Mathematics from Universidade de Sao Paulo (USP).
Steps for completing your project
After purchasing the project, send requirements so Carlos can start the project.
Delivery time starts when Carlos receives requirements from you.
Carlos works on your project following the steps below.
Revisions may occur after the delivery date.
Discovery and Architecture
Review your requirements, select STT/TTS providers, define conversation flows, and plan the system architecture.
Voice Agent Development
Build the LiveKit agent with STT, LLM, and TTS pipeline. Implement conversation logic, tool integrations, and prompt engineering.