AI ARCHITECTURE

AI is not a feature.

It is a systemic risk with value creation potential—both at the same time. And that is precisely why so many projects fail.

• Not because of the model.
• Not because of the API.
• But because of the thinking behind it.

WHY AI PROJECTS REALLY FAIL

The figures are unpleasant – and clear:

• Up to 95% of all GenAI pilots do not reach production (MIT, 2025).
• 30% of all GenAI projects are discontinued without replacement after the PoC (Gartner, 2024/25).
• Gartner already predicts a dropout rate of around 40% for Agentic AI.

“Changing Anything Changes Everything.” Sculley et al., Hidden Technical Debt in Machine Learning Systems, NeurIPS

This is no coincidence, but a structural problem.

The industry is currently producing masses of PoC corpses because AI is misunderstood as plug-and-play. The result is not a product, but hidden technical debt.

LLMs have not solved this problem—they have accelerated it.

THE REAL PROBLEM: ARCHITECTURE

In AI systems, there is usually:

• 5% model code
• 95% glue code (data preparation, prompt logic, retrieval, evaluation, monitoring, logging)

Today, this glue code is often located directly in the business logic – in other words, prompt engineering in the middle of the business logic.

Consequences

• No interchangeability of models
• No clean testability
• No explainable decision chain
• No compliance capability

In short: a system that implodes at the first model update.

MY APPROACH: ARCHITECTURE FIRST. HYPE SECOND.

I work as an AI architect at the interface of:

• Software and cloud architecture
• AI engineering
• Data and AI governance
• Law (EU AI Act, GDPR, Data Act)

Not theoretically, but in ongoing projects – including at GÖRG in AI-supported systems in the legal environment and in other regulated contexts.

I don’t build “smart demos,” but AI-enabled architectures that:

• Scale
• Are measurable (keyword: Build-Measure-Learn Circle)
• remain explainable
• are legally compliant

WHAT AN AI ARCHITECT MUST BE ABLE TO DO TODAY

TECHNOLOGY: THE FACTORY, NOT THE MODEL

An AI architect is not a data scientist. They do not build the model, but the production line.

• Model strategy

• Proprietary vs. open source (Llama, Mistral) vs. small language models

• → Decision based on risk, cost, need for control

• RAG vs. fine-tuning

• RAG: flexible, but expensive to operate

• Fine-tuning: expensive at the beginning, cheaper in inference

• → TCO calculation before project start, not after

• Evaluation & Quality

• LLM-as-a-Judge, RAGAS, Bias Checks

• Quality is measured – not felt

GOVERNANCE: LAW AS CODE

The EU AI Act is not just a compliance PDF, but a technical specification with legal consequences.

• Art. 12 – Logging & Traceability

• Architecture requires immutable protocols:

• Inputs, outputs, model version, data source, vector references

• Art. 10 – Data Quality

• Representativeness, bias control, documented thresholds

• Risk tiering (ISO 42001)

• High-risk modules are technically isolated, monitored differently, and documented differently than harmless assistance functions

Compliance here is not achieved by lawyers at the end, but by architecture at the beginning—preferably in collaboration with a lawyer who is involved from the start: me.

WHAT I DO SPECIFICALLY

• Design of resilient AI system architectures
• Clean separation of model, retrieval, prompt layer, business logic
• Governance and logging concepts by design
• Decision-making bases for costs, risk, model selection, operability
• Integration of AI Act, GDPR, and data governance – without bureaucratic drama

I prevent

• PoC graveyards
• Untestable prompt monsters
• Compliance retrofitting under time pressure
• Unpleasant legal surprises

IN A NUTSHELL

I create AI architecture for organizations that really want to use AI – not just talk about it.

For decision-makers who understand that a system is not good because it impresses, but because it can still be explained two years down the line.

And for projects that are better built cleanly today than defended expensively tomorrow.

Are you looking for someone who reads code, understands laws, and takes architecture seriously? Then we’re probably a good fit.