A greenfield delivery platform, built from zero, CloudMaven

This was a true greenfield build: a new product with no infrastructure, no pipelines and no local development setup. Everything had to be created from scratch. We built the whole delivery platform, how developers run it locally, how it ships to production, and how it recovers when something breaks.

The challenge

There was no existing infrastructure, deployment setup or automation. Everything had to be built and configured from zero.
No local development or testing environment existed, so setup was manual, inconsistent and slow to reproduce.

How it fits together

Develop

Docker one-command local env

GitHub Actions build · tag · push

Deploy · Cloud Run

Deployer auto tag + merge

9 applications sequential rollout

Shared workflow across 5 repositories, with one-click rollback to the last stable state on any failure.

Our approach

Created a dedicated dev-machine repository with Docker Compose, so any developer can clone it and bring up the full local environment in one step.
Containerized the applications with Dockerfiles validated locally.
Built shared and calling GitHub Actions workflows for automated build and deployment, with secrets managed through GitHub Secrets.
Deployed and managed the applications on Google Cloud Run.

What we built

A reusable shared CI/CD workflow integrated across 5 repositories: it builds, tags and pushes each image, then triggers a deployer repository that updates the image tag, creates a branch and auto-merges the change.
A deployer workflow that orchestrates sequential deployment of 9 applications, with an automatic rollback that reverts every app to the last stable state if any deployment fails.

The result

Eliminated manual setup: developers spin up the full system with a single command.
Reduced deployment time, with consistent builds across every repository.
More reliable releases through automated tagging, controlled image promotion and rollback on failure.
Fully automated multi-application deployments, with less manual effort and fewer chances for human error.

Where it goes next

Future improvements focus on deeper observability, with dashboards for application health and deployment status, smarter alerting to catch issues early, tighter cost control through better scaling policies, and enhanced secret management with automated rotation.

Where AI fitsThe clearest AI opportunities are automatic configuration tracking, change detection and environment-sync validation between sandbox and production, plus better audit visibility for any manual console changes, improving traceability and reducing miscommunication.

What we took away

Infrastructure and modules are not static; reusable modules need built-in flexibility so they can change without causing drift.
Some components are best left out of Terraform and managed through CI/CD instead, to keep state stable while still allowing controlled change.

Have a similar challenge?

Let's scope it together.

Tell us where your platform hurts and we'll come back with a plan. The first assessment is free.

Book a free assessment → See more stories

A greenfield delivery platform, built from zero