You will get full LLM observability and tracing setup with Langfuse or Arize
Project details
Most teams deploying LLMs in production are flying blind. They lack visibility into latency, token costs, error rates, and output quality across their AI pipelines. Without proper observability, debugging issues becomes guesswork and optimizing performance is impossible.
This project delivers a complete observability setup using Langfuse or Arize, tailored to your specific LLM stack. You get end-to-end tracing for every LLM call, custom dashboards for latency and cost monitoring, automated alerting for anomalies, and evaluation frameworks to measure output quality over time.
We have hands-on production experience with these tools. We recently deployed Langfuse tracing for a healthcare Voice AI system, achieving full pipeline visibility from speech-to-text through LLM processing to text-to-speech, with sub-500ms latency monitoring across every component.
Our team consists of senior engineers only, each with 10+ years of experience. We write production-ready instrumentation code that integrates cleanly with your existing codebase, not throwaway scripts that create tech debt. Every trace, metric, and dashboard is designed to provide actionable insights, not just data collection.
This project delivers a complete observability setup using Langfuse or Arize, tailored to your specific LLM stack. You get end-to-end tracing for every LLM call, custom dashboards for latency and cost monitoring, automated alerting for anomalies, and evaluation frameworks to measure output quality over time.
We have hands-on production experience with these tools. We recently deployed Langfuse tracing for a healthcare Voice AI system, achieving full pipeline visibility from speech-to-text through LLM processing to text-to-speech, with sub-500ms latency monitoring across every component.
Our team consists of senior engineers only, each with 10+ years of experience. We write production-ready instrumentation code that integrates cleanly with your existing codebase, not throwaway scripts that create tech debt. Every trace, metric, and dashboard is designed to provide actionable insights, not just data collection.
AI Algorithms
Large Language ModelAI Applications
AIOpsAI Development Language
PythonAI Models
GPT-4What's included
| Service Tiers |
Starter
$2,500
|
Standard
$5,000
|
Advanced
$10,000
|
|---|---|---|---|
| Delivery Time | 7 days | 14 days | 28 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 - 10:34 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
30-minute call to understand your LLM architecture, current pain points, and observability goals. We review your codebase structure and recommend the best tool and approach.
Platform Setup and Instrumentation
Configure Langfuse or Arize account. Instrument your LLM calls with tracing, metadata, and cost tracking. Integrate with your existing codebase.