You will get a production-readiness audit of your AI agent or LLM architecture
Project details
You have an AI agent or LLM feature that works in a demo. The question is whether it holds up when real users, real data and real cost hit it.
I review your architecture, code and infrastructure, then tell you straight what will break, what is over-engineered, and what to do first.
The report covers:
• Failure modes - where this breaks under real load and real data
• Cost model - what your token and infrastructure spend looks like at 10x
• Security and data handling - especially around tool calling and retrieval
• Prioritised fixes - ranked by impact, not by ease
• Build-vs-buy calls on the pieces you are unsure about
Why me: I build and run a self-hosted multi-agent platform in production - 5,700+ commits, MCP server with OAuth, plugin architecture, retrieval infrastructure. I have been shipping LLM systems since 2019 and I have been the CTO, so I will tell you when the thing you asked for is not the thing you need.
Best for teams with a working prototype heading to production, or anyone who inherited an AI system they do not fully trust.
I review your architecture, code and infrastructure, then tell you straight what will break, what is over-engineered, and what to do first.
The report covers:
• Failure modes - where this breaks under real load and real data
• Cost model - what your token and infrastructure spend looks like at 10x
• Security and data handling - especially around tool calling and retrieval
• Prioritised fixes - ranked by impact, not by ease
• Build-vs-buy calls on the pieces you are unsure about
Why me: I build and run a self-hosted multi-agent platform in production - 5,700+ commits, MCP server with OAuth, plugin architecture, retrieval infrastructure. I have been shipping LLM systems since 2019 and I have been the CTO, so I will tell you when the thing you asked for is not the thing you need.
Best for teams with a working prototype heading to production, or anyone who inherited an AI system they do not fully trust.
AI Algorithms
Large Language Model, Multimodal Large Language Model, Transformer ModelAI Applications
AI Chatbot, AI-Enhanced Classification, AI-Generated Code, AIOps, Anomaly Detection, Conversational AI, Natural Language Generation, Natural Language UnderstandingAI Development Language
PythonAI Tools
Azure OpenAI, GitHub Copilot, Hugging FaceAI Models
BERT, ChatGPT, GPT-3, GPT-4, GPT-Neo, LLaMA, OpenAI CodexWhat's included
| Service Tiers |
Starter
$750
|
Standard
$1,500
|
Advanced
$3,000
|
|---|---|---|---|
| Delivery Time | 3 days | 5 days | 10 days |
Number of Revisions | 1 | 2 | 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 Jan Hein
AI Agent and LLM Architect - RAG, MCP, TypeScript, Python
Rotterdam, Netherlands - 11:39 am local time
Behind that: 20+ years as a software architect (PhD) and founder/CTO of several startups. I have been shipping LLM-based workflows since 2019, well before the current wave, and I take ownership of the outcome, not just the tickets.
What I do:
- AI agents and LLM integration - multi-agent architectures, tool and function calling, MCP servers, agent orchestration
- RAG and knowledge infrastructure - retrieval pipelines, embeddings, organisational memory that stays accurate
- Architecture and technical review - is your approach going to survive production? Build-vs-buy, scaling, cost
- Rescue and take-over - inheriting a half-finished codebase and making it stable and shippable
- Full-stack and DevOps - polyglot: TypeScript/Node/Bun, Python, Elixir, Clojure; containers, IaC, CI/CD, self-hosted deployment
How I work:
I am senior enough to work unsupervised. I have been the CTO, so I can own a system end to end or drop into an existing team and stabilise it. I will tell you when the thing you asked for is not the thing you need.
Based in Taipei (UTC+8) - full working-day overlap with Asia-Pacific, and mornings overlap with Europe. Contracted through my Dutch company: proper invoicing, contracts and IP assignment.
Available 15-20 hrs/week, starting immediately.
Steps for completing your project
After purchasing the project, send requirements so Jan Hein can start the project.
Delivery time starts when Jan Hein receives requirements from you.
Jan Hein works on your project following the steps below.
Revisions may occur after the delivery date.
Kick-off and access
You send the repo and context. I confirm scope and flag anything I need before starting.
Review and analysis
I work through architecture, code and infrastructure: failure modes under load, cost at scale, data handling around tool calling and retrieval.