Cloud, DevOps and AI engineering you can operate after we leave
We help engineering teams build cloud platforms, delivery systems and production AI without leaving behind infrastructure nobody understands. The work lives in your repositories and cloud accounts, with architecture decisions, automation, monitoring and runbooks handed over with it.
- Infrastructure defined as code
- Repeatable, reviewable delivery
- Security and policy in the pipeline
- Handover with runbooks
Technology we build and operate with
The stack behind the engagements
Platforms and tooling we use when they fit the architecture and the operating model.
- AWS
- Microsoft Azure
- Google Cloud
- Odoo
- Docker
- Kubernetes
- Terraform
- GitHub
Four engineering disciplines
Cloud foundations, delivery systems, production AI and software engineering can run as separate engagements or as one delivery team when the work overlaps.
What you can expect during an engagement
The work stays in your environment, important decisions are written down, failure paths are tested, and handover is planned from the start.
Everything lands as code
Infrastructure, pipelines and policy live in your repositories, reviewable and reproducible — not in a console or a consultant's head.
Decisions are documented
Every significant architectural choice ships with the alternatives considered and the trade-off we accepted.
Built to be handed over
Runbooks, module documentation and pairing sessions are deliverables, so your team owns the result confidently.
Evidence over assertion
We baseline what we can measure, test the failure paths, and report honestly when a change made no difference.
Boring where it counts
Proven tooling for the load-bearing parts of a platform; novelty only where it clearly earns its operational cost.
Start with the outcome you need
Focused engagements for migration, cloud cost, platform engineering and production AI.
Where sector context changes the architecture
Industry matters when it changes data handling, availability requirements, integration constraints or load shape.
Reference architectures with the trade-offs included
These examples show how we approach recurring engineering problems. They are reference designs, not named client case studies.
Checklists and templates you can use now
Practical review artifacts for cloud cost, delivery and production AI. No email gate and no invented maturity score.
Three ways to work with us
The right model depends on how bounded the work is and who owns the roadmap afterwards.
Dedicated engineering team
A cross-functional squad working to your roadmap, embedded in your boards and review process.
Best for
Multi-quarter programmes such as a migration, platform build or product workstream where continuity matters more than a fixed scope.
- Named engineers with defined roles
- Your ceremonies, repositories and standards
- Roadmap-level planning with regular demos
Fixed-scope project
A defined outcome with agreed deliverables, acceptance criteria and a milestone plan.
Best for
Well-bounded work such as a landing zone, a CI/CD standardization effort, a migration wave or an AI evaluation harness.
- Scope defined after a short discovery
- Milestones with acceptance criteria
- Documented handover at completion
Engineering extension
Individual specialists added to your existing team to cover a capability gap or delivery peak.
Best for
Teams that own the direction and need cloud, DevOps or AI depth alongside their own engineers.
- Specialists working under your leads
- Integration into your process and tooling
- Flexible ramp up and ramp down
Notes from the engineering work
Answers to common questions
Talk to the engineers who would do the work
Bring your current architecture, constraints and the problem you are trying to solve. We will tell you what we would change first, what it depends on, and where we would start.