You will get Review your AI agent architecture and production risks
Project details
If your AI agent works cleanly in a demo but you're nervous about what happens when it meets real users, this is the right place to start. A focused call, some prep on my side, and direct feedback — we look at whatever's most load-bearing in your architecture and I'll tell you what to fix first and why, and what can wait. You walk away with a clear list of next steps. No fluff, no vague "you should think about reliability" advice.
AI Algorithms
Large Language ModelAI Applications
AI Chatbot, AI-Generated Code, Conversational AIAI Development Language
PythonAI Models
ChatGPT, GPT-4, LLaMAWhat's included
| Service Tiers |
Starter
$200
|
Standard
$400
|
Advanced
$750
|
|---|---|---|---|
| Delivery Time | 2 days | 3 days | 5 days |
Number of Revisions | 1 | 1 | 2 |
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 | - | - | - |
About Thierry
Senior AI Systems Engineer | Agent Runtime, Orchestration, Reliability
Sao Paulo, Brazil - 10:46 pm local time
For the past two years I've been an engineer at a venture-backed AI startup, building the layer underneath production AI workflows. A DAG-based workflow engine with pluggable actions: LLMs, agents, document processing, code execution, integrations. Nested sub-workflows and fan-out. Step-by-step debugging. Workflow build time went from about two weeks to 1–3 days, which finally let non-engineers ship to production.
The work I kept getting pulled into was the operationally painful stuff. A sandboxed environment for running untrusted code during agent tool-use — cold starts, timeouts, retries, automated image builds. A serverless PDF parser handling several hundred thousand pages a month, shipped with both Python and TypeScript SDKs. A document parsing proxy routing millions of pages a year across providers, with SLAs and failover built in. OpenTelemetry tracing and structured logs tuned so both humans and coding agents — Claude Code, Codex — can debug production incidents without escalation.
What I'm selling isn't the stack. It's the judgment I built doing that work, applied to your system.
How I usually help:
— architecture review on something you haven't shipped yet and you're unsure about
— production readiness audit on a system that's already live and fragile
— a focused 6–8 week sprint to build one piece right: sandbox, runtime, or observability layer
— part-time retainer as the senior AI infra voice on your team, around 10 hrs/week
I'm not the right person for a ChatGPT wrapper build or generic n8n/Zapier plumbing. If you don't need me, I'll say so — I'd rather lose the sale than bill you for work you can do cheaper elsewhere.
Stack I ship with: TypeScript, Node.js, Python, AWS serverless (SST + Pulumi), OpenTelemetry, E2B, distributed systems.
Availability: 10–20 hrs/week for a small number of clients at a time. We usually start with a short diagnostic call to figure out if I'm actually useful to you.
Steps for completing your project
After purchasing the project, send requirements so Thierry can start the project.
Delivery time starts when Thierry receives requirements from you.
Thierry works on your project following the steps below.
Revisions may occur after the delivery date.
Send your docs and repo access
You send over your architecture docs, repo access (or selected code snippets), and the top problems you want to solve.
We have the call
We do the call. I walk through the main risks I see and answer specific questions about what to fix first and what can wait.