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Docker Development

We containerize enterprise software with Docker as a deliberate engineering discipline — image builds, networking, volumes, and secrets designed for a specific production environment, not a development convenience left unchanged at deploy time.

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  • Containerized
  • Multi-stage Builds
  • Compose-Ready
  • CI/CD Native
  • Production-Grade
Overview

Our approach

We containerize enterprise software with Docker as a deliberate engineering discipline — image builds, networking, volumes, and secrets designed for a specific production environment, not a development convenience left unchanged at deploy time.

  • The Same Environment From Laptop to Production

    A container behaves identically wherever it runs, eliminating the "works on my machine" class of bugs that costs teams real debugging time.

  • A Standard Format the Whole Industry Builds Around

    Container registries, orchestrators, and cloud platforms all speak the same image format, so Docker rarely becomes the constraint on where or how something deploys.

  • Fast, Reproducible Builds

    Layer caching and multi-stage builds turn a build process into something fast and deterministic, instead of a slow step everyone dreads.

  • Isolation Without the Overhead of a Full VM

    Containers isolate processes, dependencies, and networking at a fraction of a virtual machine's resource cost, which matters directly for density and cost at scale.

  • A Natural Fit for Microservices and CI/CD

    Independently built, versioned, and deployed containers are what actually made microservices and modern CI/CD pipelines practical at scale.

  • A Mature, Battle-Tested Toolchain

    A decade of production use across the industry means the tooling, documentation, and failure modes are well understood, not a source of unknowns.

What's Included

Everything under one roof

Everything included in this engagement, from architecture to long-term support — one team, one system.

Containerized Applications

Applications packaged as containers with explicit dependencies, so what runs in production is exactly what was tested.

Development Environments

Docker Compose environments that let a new engineer run the full stack locally in minutes, not a half-day of manual setup.

CI/CD Pipelines

Build, test, and deploy pipelines built around container images as the unit of deployment, from commit to production.

Microservices

Independently deployable services, each containerized and versioned separately, without a shared runtime coupling them together.

API Platforms

Backend APIs containerized for consistent deployment across environments, with networking and secrets handled explicitly.

AI Applications

AI services and model-serving workloads containerized with their exact dependency versions, avoiding the fragility of ad hoc Python environments.

Multi-container Systems

Docker Compose or orchestrated multi-container systems where each service, database, and cache runs in its own isolated container.

Cloud Deployments

Container images deployed to managed container services or orchestrators, built once and promoted through environments unchanged.

Background Workers

Queue consumers and background job workers containerized and scaled independently from the application servers they support.

Scalable SaaS Platforms

Multi-tenant SaaS infrastructure built on containerized services that scale horizontally without a redesign per customer.
Why Aixo Lab

Why companies choose Aixo Lab

  1. We Design the Image, Not Just the Dockerfile

    Base image choice, layer order, and multi-stage build structure are deliberate decisions that determine image size and build speed, not whatever happened to work first.

  2. We Treat Secrets as a Deployment Concern From Day One

    Secrets management is architected before the first container ships, not retrofitted after credentials end up baked into an image layer.

  3. We Build for the Target Environment, Not Just the Laptop

    Networking, resource limits, and health checks are designed for how the container actually runs in production, not just how it behaves in local development.

  4. We Optimise Images for Both Build Speed and Runtime Size

    Layer caching and multi-stage builds are structured so CI stays fast and the shipped image stays lean, instead of trading one for the other.

  5. We Hand Off an Environment Your Team Can Actually Operate

    Clear Compose files, documented networking, and sane defaults mean your own engineers — or ours, later — can run and extend this system without archaeology.

Our Capabilities

Our Docker Capabilities

The specific technical capabilities behind every Docker engagement — not a generic feature list, the actual engineering surface we work in daily.

Containerization

Applications packaged into containers with explicit, versioned dependencies, so builds are reproducible across every environment.

Development Environments

Local development environments that mirror production closely enough that "it works locally" is a meaningful signal.

Production Deployments

Deployment pipelines that promote the exact image tested in CI, rather than rebuilding at each stage and introducing drift.

Docker Compose

Multi-container application definitions for local development and smaller production deployments that don't need a full orchestrator.

Multi-stage Builds

Build stages structured to keep build tooling out of the final image, cutting shipped image size and attack surface.

Container Networking

Explicit network topology between containers, so service-to-service communication is deliberate rather than accidental.

Volumes

Persistent storage attached deliberately to the containers that need it, with a clear answer for what survives a container restart.

Image Optimisation

Base image selection and layer ordering chosen to minimize image size and maximize cache reuse across builds.

CI/CD Integration

Container builds wired directly into the CI/CD pipeline, with image tagging and promotion rules that make deployments traceable.

Security Best Practices

Minimal base images, non-root users, and scanned dependencies applied as defaults, not as an afterthought before an audit.

Scalability

Container resource limits and horizontal scaling configured based on actual load patterns, not left at defaults until an incident forces the question.

Microservices

Service boundaries and container topology designed together, so splitting a system into microservices doesn't just move the complexity around.

Cloud Deployments

Container images built to run unchanged across managed container platforms, avoiding a rebuild-per-provider situation.

Observability

Logging, metrics, and health checks built into the container from the start, so production issues are visible before they become outages.
Process

How we work

The same disciplined process behind every engagement, from the first architecture decision to launch.

  1. 01
    Discovery

    Understand the business problem and its real constraints.

    Output:
    Scope and goals document
    Your involvement:
    Initial workshop
  2. 02
    Product definition

    Translate the problem into concrete product requirements.

    Output:
    Feature spec and priorities
    Your involvement:
    Requirements review
  3. 03
    UX/UI design

    Design user flows and interface before development starts.

    Output:
    Wireframes and design system
    Your involvement:
    Design feedback
  4. 04
    Technical architecture

    Define system structure, data flow, and technology stack.

    Output:
    Architecture document
    Your involvement:
    Technical review (optional)
  5. 05
    Iterative development

    Build in short cycles with visible, regular progress.

    Output:
    Regularly shipped working versions
    Your involvement:
    Sprint review participation
  6. 06
    Quality assurance

    Test functionality, performance, and security before release.

    Output:
    Test results and fixes
    Your involvement:
    Acceptance sign-off
  7. 07
    Launch

    Deploy to production with a rollback plan in place.

    Output:
    Product deployed to production
    Your involvement:
    Launch approval
  8. 08
    Continuous improvement

    Monitor, maintain, and evolve the product after launch.

    Output:
    Maintenance and improvement roadmap
    Your involvement:
    Regular check-in meetings
Technology Stack

Built on a modern, production-grade stack

Every technology here is a deliberate choice, not a default.

Docker

The containerization platform underlying every engagement built this way.

Docker Compose

Multi-container orchestration for local development and smaller production deployments.

Kubernetes

The orchestrator we reach for when a system's scale or deployment complexity outgrows what Compose or a single host can handle.

AWS

Cloud infrastructure with managed container services for deployments that need more control than a single managed platform.

GitHub Actions

CI/CD pipelines that build, test, and push container images as part of every deployment.

Next.js

A frontend framework commonly containerized alongside its backend services in a multi-container application.

React

The frontend layer for interfaces served from a containerized backend and API.

Laravel

An enterprise backend framework we containerize with its queue workers and scheduled tasks as separate services.

Node.js

A common backend runtime for containerized APIs and background workers.

PostgreSQL

The relational database of record, run as its own container in development and a managed service in production.

Redis

An in-memory store containerized alongside the application for caching, sessions, and queues.

NGINX

A reverse proxy and static file server commonly containerized as the entry point in front of application containers.
DevOps Engineering

DevOps Engineering

The engineering decisions that determine whether a containerized system stays fast, secure, and operable in production, not just on a developer's laptop.

Container Architecture

Service boundaries and container topology decided upfront, based on how the system actually needs to scale and deploy.

Image Optimisation

Base images, layer order, and multi-stage builds structured to keep images small, builds fast, and attack surface minimal.

Development Consistency

Local environments that match production closely enough that bugs show up in development instead of after deployment.

Production Deployments

The exact image tested in CI promoted through every environment unchanged, eliminating drift between staging and production.

Networking

Explicit network topology and service discovery between containers, designed rather than left to whatever the default bridge network provides.

Volumes

Persistent storage architected around what actually needs to survive a restart, with backup and ownership decided upfront.

Secrets Management

Credentials and configuration injected at runtime through a proper secrets mechanism, never baked into an image layer.

Monitoring

Container health, resource usage, and application metrics tracked so degradation is caught before it becomes downtime.

Logging

Structured, centralized logging across containers, so debugging a distributed system doesn't mean SSHing into individual hosts.

Scaling

Horizontal scaling and resource limits configured based on real load, with clear headroom before capacity becomes a production problem.

CI/CD

Container builds, tests, and deployments wired into a single pipeline, so shipping is a routine, low-risk event.
Representative Solutions

Where this technology fits

Reference architectures from our Representative Solutions collection that could plausibly be built on this stack.

AI Operations Platform
Representative Solution

AI Operations Platform

Operations & AI
Construction Project Management Platform
Representative Solution

Construction Project Management Platform

Construction Technology
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