Cloud, DevOps, and AI consulting services

Cloud migration, platform engineering, security, AI integration, and fractional leadership for EU founders, startups, scale-ups, and small businesses.

Every engagement opens with a map of what you run and how it connects. It closes when your own team runs what we built. In between, the six services fit together by design. The migration, the pipelines, the security controls, and the model end up as one platform. Nobody has to reconcile separate projects later.

The pricing page lays out the six packages my engagements usually fall into. Some don't fit any of them, and that's fine. A thirty-minute call usually sorts out the rest. Which part of your platform would you fix first?

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Connected by design

Infrastructure isn't six separate projects.

Cloud foundations, delivery pipelines, security controls, model integrations, and ownership need to work together. I map the interfaces before I change the components.

Cloud migration

I move teams onto AWS, Azure, or Google Cloud, or out of a setup they've outgrown. I map every dependency before anything moves.

On the AWS migrations I delivered as a consultant, client NPS went from 8 to 10. That result didn't come from the interesting parts. It came from the boring ones: accounts nobody owns, a network that grew by accident, a dependency nobody drew on the diagram. So I map what you actually run before anything moves. Once the map is accurate, the right order of moves is easy to see.

Readiness assessment and a target architecture your team can run
Landing zone, identity, and networking in an EU region
Multi-account AWS foundations in Terraform, ready for due diligence
Migration in waves, with a rehearsed cutover
Off Heroku, a single server, or on-premises hardware, awkward workloads included
Legacy code modernised onto managed services along the way

Platform engineering

I do cloud engineering on the platform you already run. I make it calmer, cheaper, and understood by more than one person on the team.

I've run this layer at real scale: 200+ microservices on Terraform-built EKS, and 2,000+ production instances at 100% SLA compliance with a 10-person team. I'm starting to see a lot of scale-ups struggle with the same fragile, undocumented, one-person platform. The pipelines need a human in the loop, and the alerts wake the one engineer who knows how it all fits. A 3 a.m. page is great for team morale. Said no one ever.

Infrastructure-as-Code and CI/CD pipelines
GitOps delivery on Kubernetes: EKS, ArgoCD, GitHub Actions
Kubernetes and serverless right-sizing
Monitoring, logging, and alerting a small team can live with
Disaster recovery and data protection, automated and tested
Cost reviews that stretch the runway

Cloud security hardening

I find the gaps in your cloud accounts, identity, network, and data, rank them by what an attacker reaches first, and close them with your team.

Most cloud estates I open have the same shape. Nobody is careless. The accounts grew one urgent change at a time, and the security posture is whatever that history left behind.

Posture assessment across accounts, identity, network, and data
Findings ranked by what an attacker reaches first, not by scanner count
Least-privilege IAM, single sign-on, and no long-lived keys
Encryption, backups, and storage access hardening, written as code
Logging and detection a small team will actually read
Remediation with your team, plus the evidence enterprise questionnaires ask for

AI-built app security review

I review the app your AI coding agent wrote, from secrets and sign-in to the cloud account behind it, and rank what to fix first. From €500.

AI coding agents write working apps fast. Working isn't the same as safe. The agent builds what you asked for, and nobody asked it who should be allowed to read each record. I'm starting to see the same handful of gaps in a lot of AI-built apps.

Secrets, keys, and tokens found in the code and its history
Sign-in and API authorisation checked route by route
Dependencies checked for known flaws and invented packages
The coding agent's own permissions and the CI pipeline
The cloud account the app runs in
Findings ranked by what an attacker reaches first, with the fixes if you want them

AI integration

I do AI consulting and AI engineering for EU teams. I put language models to work on the problems that matter, and I ship them to production.

I used to think the model was the hard part of AI work. After building a marketplace on one, I realised it's the easy part. The real work is choosing which problem deserves a model at all. Then comes getting your own data in front of it, keeping it inside guardrails, and keeping European customer data where it belongs. The question I ask first isn't what's possible but what matters to your users and your runway.

Retrieval over your own documents and data
Vector search and embedding pipelines in production
Claude or OpenAI-compatible APIs, chosen per use case
Agents that take real actions, with guardrails
Evaluation, so you know when it works
The app and API around the model, not only the prompt

Fractional cloud leadership

Cloud architecture and a senior cloud lead on a fixed monthly retainer, long before a full-time cloud team makes sense.

I'm starting to see a lot of startups and scale-ups hit the same wall. They need a cloud lead years before a cloud team makes sense. Someone has to sit in the architecture reviews, say no to the wrong vendor, and answer the enterprise security questionnaire. That person also has to keep delivery honest and hold cloud, delivery, and AI in one picture. A fixed monthly retainer, built around agreed deliverables, usually covers it.

Architecture and security reviews
Vendor and tooling decisions
Delivery oversight alongside your engineers and product leads
Ops leadership: a 10-person team, 2,000+ instances, 100% SLA
A founder's view of budget, runway, and product
Coaching for the team who will own it

Let's talk.

A 30-minute call, no pitch. We look at your current setup, your stage, and the thing that's bothering you about it. I give a straight opinion on what to change, what to leave alone, and whether the problem is the one it looks like. What's the one thing in your stack you'd fix tomorrow?

Book a call See pricing

Prefer email or phone? hello@netninjas.eu or +31 6 45984313