Still developing without AI? Your competitors aren't.
While your team searches, reviews, and waits — the team next door ships with AI. The productivity gap grows every day. Estimates show AI-assisted developers are 2–5× more productive.
You know this. But your IT department says no — and they're right:
- ● Cloud-based AI coding tools send your source code to third-party servers
- ● GDPR, BaFin, TISAX, DORA — every regulation blocks it
- ● Developers are 30–50% slower than the competition
- ● Top talent gets frustrated — and moves to companies that allow AI
What if AI coding were possible without compromise?
I install a complete AI coding assistant inside your infrastructure — not a single byte leaves your network.
Code stays with you — always
On-premises or private cloud. Zero external data transfer.
GDPR-compliant by design
No Schrems II risk. No US CLOUD Act exposure. No BaFin issue.
Enterprise AI quality — internal
Same productivity gains as leading AI coding tools — controlled and approvable by your IT.
One-time investment
Comparable to per-seat SaaS subscriptions — but paid once, not monthly.
A Day in the Life — With and Without AI
| Task | Without AI | With AI |
|---|---|---|
| Unfamiliar library | 45 min searching, trial and error | "Show me an example" — 2 minutes |
| Bug since yesterday | 4 hours debugging, asking colleagues | Paste context, root cause in 5 minutes |
| Writing boilerplate | Copy-paste, adapt, forget what's what | Generated, explained, ready to use |
| Preparing code review | Re-read everything, write comments | "Review this code" — done |
| Understanding new codebase | Days to weeks of reading | "What does this class do?" — instantly |
| Writing unit tests | Tedious, postponed, often forgotten | Generated as you code |
| Regex / SQL / Config | Google, test, curse | Correct on first try, with explanation |
| Documentation | Nobody wants to do it | Auto-generated from code |
The Real Cost Calculation
The cost has two parts: my work, paid once for audit, setup, and onboarding — and ongoing infrastructure costs per developer to run the model in your cloud.
| Option | Cost | Result |
|---|---|---|
| Compliance Coder | One-time: my work Ongoing: ~€120/developer/month infrastructure |
Team becomes productive — GDPR-compliant |
| Cloud AI coding tool | €19/developer/month | IT blocks it — zero value |
| No AI tool | €0/month | Developers 20–30% slower |
Technology used
- •Model: Qwen2.5-Coder 14B — open-source language model, purpose-trained for software development
- •Infrastructure: Azure or AWS, GPU instance with NVIDIA T4 16GB
- •Data residency: Fully within your own cloud environment — no external connections
"Your developers get a private AI assistant. IT is happy. BaFin/DORA audit is clean. Cost: my work once, then ~€120/developer/month."
Live in 3 Steps
Audit — 1–2 days
We analyse your infrastructure, compliance requirements, and security policies to design the right setup.
Setup — 2–3 days
Deployment in Azure, AWS, or on-premises. Everything configured, secured, and tested in your environment.
Team Onboarding — 1 day
Hands-on workshop for your developers. Editor integrations configured. Team shipping faster from day one.
Pricing
My day rate is in the standard market range for experienced freelancers. The key difference: it's a one-time investment. Based on experience, the productivity gains in the development team typically pay back my fee within a few weeks.
Starter
Small team, single environment. Audit, setup, and onboarding for up to 10 developers.
Professional
Mid-size team, multiple environments (dev/staging/prod). Includes extended coaching and 30-day support.
Enterprise
Large teams, air-gapped or complex on-premises environments. Includes compliance documentation.
Exact pricing depends on your infrastructure and team size. Get in touch for a free assessment.
Model quality: on par with ChatGPT and Claude
A common concern is that local open-source models are significantly inferior to ChatGPT or Claude in quality. For coding tasks, that is no longer true. The Qwen2.5-Coder 14B used here scores ~89% on the industry benchmark HumanEval — comparable to GPT-4o and well above older models like DeepSeek Coder 33B (70%) or CodeLlama 34B (53.7%), which require three to five times the compute.
| Model | HumanEval | Context | Assessment |
|---|---|---|---|
| Qwen2.5-Coder 14B (used) | ~89% | 128K | Comparable to GPT-4o for code |
| Qwen2.5-Coder 32B | 92.7% | 128K | Optional upgrade with a larger GPU |
| DeepSeek Coder 33B | 70% | 16K | Older, significantly weaker |
| CodeLlama 34B | 53.7% | 16K | Outdated, not recommended |
Benchmark data from independent analysis at hardware-corner.net. HumanEval is the established industry standard for measuring code correctness in language models.
Why you can trust me with this
- • 20+ years of software engineering across Java, Ruby, iOS, and JavaScript/TypeScript
- • Experience in regulated industries across the DACH region: fintech, automotive, SaaS
- • Former CTO — I understand both the technical and the organisational side
- • Freelance since 2015 — outside perspective with inside accountability
- • Your IT can approve it. Your developers will love it.
Ready?
Multiply the productivity of every developer on your team.
No Stack Overflow. No waiting for a colleague. No explaining why the tool worked yesterday.
Just: writing real code. Solving real problems. Every day.
30 minutes. No pitch, no pressure.