You will get Audit your AI agent for production readiness and reliability
Project details
Most AI agents are built phase by phase. Someone gets a demo working. Someone adds a tool call. Someone hooks up a vector DB. By the time you ship, the architecture is whatever emerged, and nobody really knows what will happen under load or when something fails weirdly. I audit that. I look at architecture, code, and — when you have them — real logs and traces. The things most likely to hurt you in production: bad failure propagation, silent retries that multiply cost, state handling that breaks under concurrency, tool-use paths with no isolation, token budgets with no ceiling, debugging pathways that don't exist yet. You get a prioritized report plus a call to walk through it.
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
$1,800
|
Standard
$4,500
|
Advanced
$6,500
|
|---|---|---|---|
| Delivery Time | 5 days | 10 days | 12 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 | - | - |
About Thierry
Senior AI Systems Engineer | Agent Runtime, Orchestration, Reliability
Sao Paulo, Brazil - 9:23 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.
Access and context
You share your architecture, code access, logs or traces if available, and your current stack.
I do the audit
I go through the architecture, the code, and any logs or traces. I find the things most likely to break in production and rank them by impact.