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Architecture, Development & DevOps

Shipping fast and shipping safely are not opposites.

Immutable architectures, CI/CD pipelines with security built in, and teams that start releasing versions without holding their breath.

The real problem

Most teams do not choose between speed and safety: they simply lose both. Deployment is rare because it is frightening, and it is frightening because it is rare. Lead time grows, the change batch fattens, and every release becomes a risk event.

We work alongside the development team on the design, implementation and adaptation of architectures for web and online applications, with a real infrastructure foundation underneath, thinking about high availability, scalability and capacity from the first sketch.

Our format works both for a single project and for a long transformation: a new solution, adapting an existing one, best-practice consulting, building a complete pipeline, placing DevOps engineers, or training the team on site.

What we deliver

The scope, with no fine print.

Architecture

  • Design and adaptation of infrastructure architecture with HA and DR
  • Architectures that are immutable, scalable and secure by default
  • APIs, integrations and microservices
  • Management and control of microservices in production
  • Autoscaling sized from load testing, not from guesswork

Pipeline and automation

  • Building complete CI/CD pipelines
  • Automation on AWS, Google Cloud and Azure
  • Automation in virtual environments and private clouds
  • Dashboards for centralised log control
  • Stress tests wired into the build

DevSecOps

  • Pipeline adapted with SAST and DAST testing
  • An S-SDLC process for the development cycle
  • Secret and credential management outside the code
  • SRE practice with agreed error budgets and SLOs
  • Team training and living documentation of the environments

The AI layer

Where AI comes in: assisted engineering

Development with AI and no discipline becomes technical debt at record speed. We deploy the practice together with the tool: where the agent may write code, what always goes through human review, how the test is written before the prompt, and which metric decides whether it reaches production.

See the AI Transformation journey
  • A defined review standard for AI-generated or AI-assisted code
  • Tests and acceptance criteria written before the implementation
  • Assisted pull request analysis, focused on risk and regression
  • Documentation and runbooks generated from the code itself
  • Micro SaaS and internal tools that previously never paid off

Advantages

What changes for you.

This is not a feature list. It is what becomes different in the operation once the practice is running.

  • DevSecOps practices used by companies such as Google, Spotify and Meta
  • Risk reduced at points of the cycle where there is no visibility today
  • New versions live with no downtime for the end customer
  • Shorter lead time between the idea and what is actually in production
  • Traffic grew? The environment adjusts itself, within defined limits
  • Experienced Agile Coaches supporting the team through the cultural change

Ask for a free assessment of your environment :)

A technical assessment of the current environment, with a direct read: what AI can automate, what needs a foundation first, and what is not justified at this point.