Make your infrastructure boring. Then put AI to work on it.
Boring means it deploys on Friday, survives a reboot, and doesn't depend on one person's memory. We take over, fix and run cloud and Kubernetes platforms on AWS, Google Cloud and Azure, then build AI on top that your security team will sign off on.
- Reply within one business day
- NDA before any access
- EU company, GDPR-compliant
- 11+ yrs
- running production infrastructure
- 25,000+
- bare-metal servers operated at hosting scale
- 2,500+
- server and site migrations delivered
- EU + US
- time-zone overlap, every working day
Founder's track record across previous infrastructure roles.
Usually it's one of these.
Servers nobody dares reboot, pipelines nobody understands, a cron job that might be important.
You want CI/CD and infrastructure as code, but nobody has time to set it up properly.
To AWS, Google Cloud or Azure, to Kubernetes, or off a platform you have outgrown.
Or the pilot worked in a demo and quietly died before it reached real users.
Plenty of AI demos work. Far fewer make it to production.
Connecting a chatbot to an API takes an afternoon. Running it for real customers raises harder questions about where the data lives, who is allowed to see it, what it costs each month and what happens when it gets something wrong. We come from infrastructure, so we start with those.
Private LLMs & RAG
Ask questions of your own contracts, manuals and tickets. Open-weight models on your cloud or on-prem, or OpenAI and Anthropic with the right data controls in place.
Agents & automation
Agents that triage tickets, fill in forms, draft replies and pull context from your systems. A person approves anything that matters.
GPU & MLOps infrastructure
Model serving, GPU scheduling on Kubernetes, pipelines and cost controls. We've run GPU workloads for NLP in production.
AI readiness audit
A two-week, fixed-price review of which use cases would pay off, what your data allows and what each option costs to run. It also tells you what not to build.
And if AI isn't the right answer to your problem, we'll say so on the first call. A good script is sometimes cheaper than a model.
Four ways to work with us
Every engagement has a fixed scope and a price agreed before we start. Most clients begin with an audit, and keep the report whatever they decide next.
Infrastructure Audit
START HERERead-only access, 5–10 working days. What you actually run, and what will hurt you first.
- Full inventory and architecture diagram
- Risks ranked by impact, with evidence
- Cloud cost review
- 30/60/90-day plan
DevOps & Cloud Build
CI/CD, infrastructure as code and Kubernetes, set up properly once.
- Terraform and Ansible for everything
- Pipelines in GitHub, GitLab or Azure DevOps
- Migrations to AWS, GCP or Azure
- Handover docs your team can follow
AI Pilot
One use case, from idea to real users in 4–6 weeks.
- Use case and success metric agreed upfront
- Data pipeline and retrieval
- Deployed on your cloud, with access controls
- Cost and quality monitoring
Platform Operations
Your platform team, without hiring a full-time SRE.
- Patching, upgrades, certificates
- Kubernetes upgrades and GitOps
- Response times agreed in writing
- Monthly report: changes, risks, costs
Code, Terraform, pipelines and docs live in your repos and your accounts. Never ours.
Monthly contracts. If you leave, we hand over cleanly and help the next team.
We look before we touch. Every change after that goes through review.
Fixed scope and fixed fee. Any change to the scope is agreed in writing before extra work starts.
We don't trust the documentation. We verify it.
Every fact in our reports carries a status: what we proved ourselves, what we only saw, what someone told us, and what nobody knows yet. You see exactly where the risk sits.
We built our own tool for this, InfraOps. Its AI assistant always tells you how it knows what it's telling you, so nobody mistakes a guess for a fact about your production.
You tell us what you run or what you want to build. We tell you honestly if we're the right fit.
NDA first, read-only access. We change nothing while we look.
A walkthrough call, the full report, and a fixed price for the next step.
We do the work, or we give your team a plan they can execute themselves.
Ciprian Potor
I've spent more than eleven years keeping production systems alive, and more than eight writing software. I studied computer science, and I still read the application code before I blame the infrastructure.
Along the way I ran bare-metal operations across 25,000+ servers at a large US hosting provider, handled thousands of migrations at a managed cloud company and built GPU infrastructure for NLP and machine-learning workloads. These days I work on enterprise Kubernetes platforms, hybrid cloud and SSO systems used in healthcare.
Because I come from both sides, I look at a production problem as a whole: the application code, the operating system, the network and the platform underneath. Most outages live in the gaps between those layers.
Lately, much of my time goes into AI engineering: deploying LLMs on GPUs, and building agent workflows that take repetitive work off people's desks. I'm also building InfraOps, our own platform for evidence-based infrastructure takeovers.
Understand the systems beneath the abstractions. Write code that solves real problems. Automate what makes sense. Build infrastructure that stays reliable when things go wrong.
Center Process is founder-led. You talk to the engineer doing the work, not an account manager. When a project needs more hands, I bring in senior specialists I've worked with before, and you'll know exactly who they are.
Before you book a call
Do you work with AWS, Azure and Google Cloud?
Yes, all three, plus bare metal. We'll recommend whichever one fits your team and budget.
Can we use AI without our data going to OpenAI, Anthropic or anyone else?
Yes. Open-weight models such as Llama, Qwen or Mistral can run entirely on your own servers or private cloud, so nothing leaves your network. If you'd rather use a managed model, Azure OpenAI, AWS Bedrock and Google Vertex AI process requests inside your own cloud account and region, under enterprise terms where your data isn't used for training. We compare quality, cost and compliance for each option before you choose.
Which time zones do you cover?
We're in Romania (EET). Full overlap with Europe, and morning overlap with the US East Coast.
What if it turns out we don't need you?
Then you have a clear report and a plan your own team can execute. That's a good outcome too.
Tell us what you're running, or what you want AI to do.
20 minutes with an engineer, not a salesperson. No slides, no obligation.