Modernize your IT with DevOps + AI (MLOps / LLMops)
We support customers to modernize software delivery embedding AI solutions from prototype to production. Our focus is on reliable DevOps practices and production-grade AI operations (with human in the loop where needed) , and keep them compliant regarding data sovereignty and infrastructure requirements.
DevOps that ships faster, with control
With CI/CD, automation and release management teams can deliver updates more frequently and with fewer production issues. The result: faster time-to-market, better operational stability, and clearer governance across environments. AI assisted can further improve and automate dev and ops deliverables.
AI transitions that respect European data boundaries
Besides AI models AI in production requires also pipelines, evaluation, monitoring, and governance.
Properly designed end-to-end MLOps and LLMops workflows will result in AI systems are testable, measurable, and maintainable over time.
This all on infrastructure that supports cost effective technical hosting needs with respect to privacy and data protection.
MLOps & LLMops, end-to-end
- MLOps: automated model lifecycle (data → training → deployment → monitoring → retraining)
- LLMOps: safe, evaluated LLM workflows (prompt/version management, quality & safety checks, cost control, continuous improvement)
European-ready platform foundations
The engineering parts that AI needs to operate reliably in production are: automated pipelines, infrastructure as code, containerized deployments, observability, and security-by-design, so teams can iterate quickly without losing control.
Why this matters for Europe (data sovereignty + hosting)
We support solutions where AI workflows and infrastructure can be hosted in Europe, aligning with your compliance and data-handling expectations.
We also build on major European enablers like:
- n8n for AI workflow automation
- Hetzner for affordable cloud hosting
AND - Nebius for AI cloud hosting
Deliverables
- DevOps modernization (CI/CD, automation, release control)
- AI operations (MLOps / LLMops processes and governance)
- Model & prompt versioning, automated evaluation, and continuous monitoring
- Data/quality observability and operational trust in production
- Secure, European-hosting-aligned deployment approaches
Engagement options
- Assessment & roadmap: evaluate your current setup and define a practical DevOps + AI transition plan
- Pilot implementation: deliver a focused pilot to prove value before scaling
- Ongoing managed improvement: continuous optimization for reliability, security, and performance