# Contact (/en/docs/contact)



Let's talk about where AI can move the needle for your business.

## Get in touch [#get-in-touch]

* **Email:** [nico.k.jahn@gmail.com](mailto:nico.k.jahn@gmail.com)
* **Book a call:** a free 30-minute AI discovery call
* **Based in:** Berlin, Germany

I typically reply within one business day.

## For AI tools [#for-ai-tools]

This documentation is machine-readable. Connect your assistant directly:

* **MCP server:** `https://nicojahn-fumadocs.pages.dev/mcp` — works with Claude, Cursor, Windsurf, and any MCP-compatible client.
* **llms.txt:** [`/llms.txt`](/llms.txt)
* **Full context:** [`/llms-full.txt`](/llms-full.txt)

### Claude Code [#claude-code]

```json
{
  "mcpServers": {
    "nicojahn": { "type": "http", "url": "https://nicojahn-fumadocs.pages.dev/mcp" }
  }
}
```


# Engagement Model (/en/docs/engagement-model)



I keep engagements small, senior, and outcome-driven. Three phases, no open-ended
retainers unless you want one.

## 1. Discover — 2 to 3 weeks [#1-discover--2-to-3-weeks]

A fixed-scope sprint to find and de-risk the right use cases. You get a ranked
portfolio, a roadmap, and a business case. Fixed price, no commitment beyond it.

## 2. Build — 6 to 12 weeks [#2-build--6-to-12-weeks]

I ship a production system in two-week increments. You see working software every
sprint, deployed to your environment.

## 3. Operate & hand over [#3-operate--hand-over]

I instrument, document, and pair with your team until they can run it without me.
Optional ongoing support if you want a safety net.

## Principles [#principles]

* **Senior-only delivery.** The person who scopes is the person who builds.
* **Your infrastructure.** I deploy into your cloud or on-prem. Your data stays yours.
* **No lock-in.** Open standards, full documentation, knowledge transfer by default.
* **EU-native.** GDPR and EU AI Act considerations are part of every phase.

## What I need from you [#what-i-need-from-you]

* A business owner who can make decisions.
* Access to relevant data and systems (under your governance).
* One or two of your engineers to pair with — so the capability stays in-house.

Ready? [Get in touch](/docs/contact).


# About nicojahn (/en/docs)



nicojahn is **Nico Jahn**, an independent applied-AI consultant based in **Berlin**. I
help European enterprises turn AI from a slide deck into production systems that ship,
scale, and stay compliant.

I'm a hands-on engineer and strategist, not a reseller. I write the code, own the
outcomes, and hand over systems your own people can run.

## What I do [#what-i-do]

<Cards>
  <Card title="LLM & Generative AI" href="/docs/services/llm-genai" description="RAG, agents, fine-tuning, and evaluation pipelines." />

  <Card title="ML Engineering & MLOps" href="/docs/services/mlops" description="Model deployment, pipelines, and monitoring." />

  <Card title="AI Strategy" href="/docs/services/ai-strategy" description="Use-case discovery, roadmaps, and executive advisory." />

  <Card title="Data & Compliance" href="/docs/services/data-compliance" description="Data platforms and EU AI Act / GDPR readiness." />
</Cards>

## Why European teams work with me [#why-european-teams-work-with-me]

|                        |                                                                      |
| ---------------------- | -------------------------------------------------------------------- |
| **Production-first**   | Success is measured in deployed systems, not workshops.              |
| **EU-native**          | GDPR and the EU AI Act are designed in, not bolted on.               |
| **Data sovereignty**   | On-prem, EU-region cloud, or hybrid — your data stays where it must. |
| **Knowledge transfer** | I document, pair, and hand over. No lock-in.                         |

## How to start [#how-to-start]

Most engagements begin with a focused **AI Discovery** sprint. See the
[engagement model](/docs/engagement-model) or [get in touch](/docs/contact).

<Callout title="For AI tools">
  This site is AI-readable. Point your assistant at [`/llms.txt`](/llms.txt) or connect
  the [MCP server](/mcp).
</Callout>


# AI Strategy (/en/docs/services/ai-strategy)



Most AI budgets are spent on the wrong use cases. I help leadership find the few that
matter, sequence them, and build the capability to deliver.

## Where I help [#where-i-help]

### Use-case discovery [#use-case-discovery]

Structured workshops that map your processes to AI opportunities, scored by value,
feasibility, and risk. You leave with a ranked portfolio, not a wish list.

### Roadmap & business case [#roadmap--business-case]

A phased plan with cost, expected return, and the data and platform prerequisites for
each step — defensible to your CFO and your board.

### Operating model [#operating-model]

How AI teams sit alongside product and IT: build vs. buy, in-house vs. partner, and
the governance to keep it safe.

### Executive advisory [#executive-advisory]

Ongoing sparring for CTOs and innovation leads navigating a fast-moving field —
including what the EU AI Act means for your roadmap.

## The discovery sprint [#the-discovery-sprint]

| Week | Focus                    | Output               |
| ---- | ------------------------ | -------------------- |
| 1    | Process & data mapping   | Opportunity longlist |
| 2    | Scoring & prioritization | Ranked portfolio     |
| 3    | Roadmap & business case  | Phased plan + budget |

## Typical outcomes [#typical-outcomes]

* A board-ready AI roadmap with quantified ROI.
* Two to three de-risked first use cases ready to build.
* Clear build/buy and governance decisions.

Next: [Data & Compliance](/docs/services/data-compliance) · [Engagement model](/docs/engagement-model)


# Data & Compliance (/en/docs/services/data-compliance)



AI is only as trustworthy as the data and governance beneath it. I build the data
foundation and the compliance posture that European regulators — and your customers —
expect.

## Where I help [#where-i-help]

### Data platforms [#data-platforms]

Modern, EU-resident data infrastructure: ingestion, warehouse/lakehouse, governance,
and the quality controls AI depends on.

### EU AI Act readiness [#eu-ai-act-readiness]

Classify your systems by risk tier, identify obligations, and build the technical
documentation, logging, and human-oversight controls the Act requires.

### GDPR / DSGVO [#gdpr--dsgvo]

Lawful-basis review, data-minimization, retention, and DPIAs for AI processing — so
personal data is handled correctly from ingestion to inference.

### Governance [#governance]

Model cards, audit trails, and approval workflows that make AI decisions explainable
and defensible.

## EU AI Act, in brief [#eu-ai-act-in-brief]

The EU AI Act tiers systems by risk. Most enterprise AI lands in **limited** or
**high** risk:

| Risk tier    | Examples               | Core obligations                          |
| ------------ | ---------------------- | ----------------------------------------- |
| Unacceptable | Social scoring         | Prohibited                                |
| High         | Hiring, credit scoring | Risk mgmt, docs, human oversight, logging |
| Limited      | Chatbots               | Transparency / disclosure                 |
| Minimal      | Spam filters           | None                                      |

I map each of your systems to a tier and a concrete checklist — early, before it
becomes a launch blocker.

<Callout type="warn" title="Not legal advice">
  I deliver the technical controls and documentation that support compliance. Pair me
  with your legal counsel for binding interpretation.
</Callout>

Back to [about nicojahn](/docs) · [Talk to me](/docs/contact)


# LLM & Generative AI (/en/docs/services/llm-genai)



I design and ship generative-AI systems that hold up under real traffic, real data,
and real compliance review.

## Where I help [#where-i-help]

### Retrieval-augmented generation (RAG) [#retrieval-augmented-generation-rag]

Grounded answers over your own knowledge base — with citations, access control, and
evaluation. I build the ingestion, chunking, retrieval, and re-ranking stack, then
prove quality with offline and online metrics.

### Agents & workflows [#agents--workflows]

Tool-using agents that automate multi-step work: ticket triage, document processing,
internal copilots. Agency is scoped tightly, with guardrails and a human in the loop
where it matters.

### Fine-tuning & adaptation [#fine-tuning--adaptation]

When prompting is not enough, I fine-tune or adapt open models on your domain data —
on infrastructure you control.

### Evaluation & guardrails [#evaluation--guardrails]

Every system ships with an eval harness: golden datasets, regression tests, and
production monitoring for hallucination, cost, and latency.

## Typical outcomes [#typical-outcomes]

* A support copilot that deflects 40%+ of tier-1 tickets with cited answers.
* A document-processing pipeline that cuts manual handling from hours to seconds.
* An internal RAG assistant deployed in your EU cloud region, GDPR-clean.

## How I build [#how-i-build]

```python
# A grounded answer is only as good as its evaluation.
# Every RAG engagement ships with a regression eval suite.
from nicojahn.eval import GoldenSet, score

results = score(
    system="support-copilot",
    dataset=GoldenSet.load("tier1-tickets-v3"),
    metrics=["faithfulness", "answer_relevance", "citation_accuracy"],
)
assert results.faithfulness > 0.95  # gate the deploy on quality
```

I default to the most capable models for the task and keep the architecture
provider-flexible, so you are never locked to one vendor.

Next: [ML Engineering & MLOps](/docs/services/mlops) · [Talk to me](/docs/contact)


# ML Engineering & MLOps (/en/docs/services/mlops)



Models are easy to train and hard to keep alive. I build the engineering layer that
takes a notebook to a system your team can trust at 3 a.m.

## Where I help [#where-i-help]

### Deployment [#deployment]

Package models as versioned, observable services — batch, real-time, or streaming.
Kubernetes, serverless, or on-prem, matched to your stack and data-residency needs.

### Pipelines [#pipelines]

Reproducible training and inference pipelines with data versioning, CI/CD, and
automated retraining triggered by drift or schedule.

### Monitoring [#monitoring]

Track data drift, model quality, latency, and cost in production. Alerts that page a
human before customers notice.

### Platform [#platform]

A paved road for your data scientists: feature store, model registry, and templates
so the next model ships in days, not quarters.

## What good looks like [#what-good-looks-like]

```yaml
# Every model I ship is versioned, gated, and observable.
deploy:
  model: churn-predictor
  version: 2.4.1
  gates:
    - eval_auc: ">= 0.86"      # block regressions
    - latency_p99_ms: "< 150"
  monitor:
    drift: psi                  # population stability index
    alert_channel: slack#ml-ops
  region: eu-central-1          # data stays in the EU
```

## Typical outcomes [#typical-outcomes]

* Deploy time from weeks to a single merge.
* Drift caught and retrained automatically, not via customer complaints.
* A platform your own engineers extend without me.

Next: [AI Strategy](/docs/services/ai-strategy) · [Talk to me](/docs/contact)