Project Overview
| Client Industry | Technology / B2B SaaS |
| Business Type | Project management SaaS platform, ~40-person engineering team, 25,000+ active users |
| Project Duration | 10 weeks |
| AI Service Provided | CI/CD Pipeline Automation |
| Technologies Used | GitHub Actions, Docker, AWS (ECS, ECR, CloudWatch), Terraform, SonarQube, Jest, Cypress |
The Client Challenge
The client’s engineering team shipped new features and fixes through a manual, ticket-driven release process that hadn’t scaled with the team’s growth from 8 to 40 engineers.
The operational reality:
- Releases happened once every 2–3 weeks, bundling dozens of unrelated changes into a single high-risk deployment.
- Deploying to production required a specific engineer to manually run a 45-minute checklist of build, test, and server commands creating a bus-factor risk and release bottleneck.
- Test suites ran inconsistently; some engineers skipped local testing before merging, leading to broken builds discovered only after merge.
- A single bad release could take 3–4 hours to identify, roll back, and fix, during which customers experienced degraded service.
- The engineering team wanted to move toward continuous delivery but had no confidence their existing test coverage or deployment process could support faster releases safely.
Feature velocity was suffering engineers were finishing work that then sat for up to two weeks waiting for the next release window.
Our Solution
Air Brite Labs designed and built a fully automated CI/CD pipeline that takes code from commit to production deployment with automated testing, quality gates, and rollback safety built in replacing the manual release ritual with a repeatable, low-risk process.
Every pull request now triggers automated builds, tests, and code quality checks before merge is even possible. Merges to the main branch automatically build, test, and deploy to staging; production deployment requires a single approval click rather than a 45-minute manual process.
Key capabilities:
- Automated PR checks — every pull request runs unit tests, integration tests, and static code analysis before it can be merged, catching issues at the source.
- Progressive deployment pipeline — code flows automatically from commit → staging → production with defined quality gates at each stage.
- One-click production deploys — what was a 45-minute manual checklist is now a single approval step with full visibility into what’s being deployed.
- Automated rollback — failed health checks post-deployment trigger an automatic rollback to the last stable version without manual intervention.
- Deployment visibility dashboard — engineers can see deployment status, test results, and history without digging through logs.
Technical Approach
Pipeline Architecture: Built on GitHub Actions, with separate workflows for PR validation, staging deployment, and production deployment. Each workflow runs in isolated, ephemeral containers for consistency.
Containerization: Application services were containerized with Docker and pushed to Amazon ECR, standardizing what had previously been inconsistent manual server configurations across environments.
Deployment Target: AWS ECS handles container orchestration for staging and production, with blue-green deployment configured so new versions roll out alongside the running version and traffic shifts only after health checks pass.
Testing Integration: Jest for unit and integration tests, Cypress for automated end-to-end tests against the staging environment, and SonarQube for static code analysis and code quality gating all wired directly into the pipeline as required checks.
Infrastructure: Underlying AWS infrastructure (ECS clusters, load balancers, networking) provisioned and version-controlled with Terraform, so environment configuration itself is auditable and repeatable.
Monitoring & Rollback Safety: AWS CloudWatch alarms monitor error rates and response times post-deployment; a failed health check within the first 10 minutes triggers automatic rollback to the previous stable ECS task definition.
Implementation Process
- Discovery & Requirement Analysis (Weeks 1–2): Audited the existing manual release process, current test coverage, and infrastructure setup; identified gaps in automated test coverage that needed addressing before pipeline automation would be safe.
- Prototype / PoC (Weeks 3–4): Built the PR-check pipeline first, validating automated testing on a subset of services before touching production deployment.
- Development (Weeks 5–7): Built the full staging and production deployment pipelines, containerized remaining services, and implemented the blue-green deployment strategy.
- Integration (Week 8): Connected SonarQube quality gates and CloudWatch monitoring/rollback triggers into the pipeline.
- Testing (Week 9): Ran the new pipeline in parallel with the legacy manual process for two release cycles, comparing reliability and catching edge cases.
- Deployment (Week 9): Cut over fully to the automated pipeline, retiring the manual release checklist.
- Optimization (Week 10 and ongoing): Tuned pipeline speed by parallelizing test suites and caching dependencies, cutting average pipeline run time further.
Key Features Delivered
- Fully automated CI/CD pipeline from commit to production
- Automated PR validation with unit, integration, and static analysis checks
- Containerized services with standardized Docker builds across environments
- Blue-green production deployment with automated health-check-based rollback
- One-click deployment approval replacing a 45-minute manual process
- Infrastructure as Code for all pipeline-supporting AWS infrastructure
- Real-time deployment visibility dashboard for the engineering team
Business Results
- Release frequency increased from once every 2–3 weeks to multiple times per day
- Average deployment time dropped from 45 minutes of manual work to under 8 minutes, fully automated
- Bad-release recovery time fell from 3–4 hours to under 10 minutes via automated rollback
- Broken-build incidents caught before merge increased significantly, reducing post-merge firefighting
- Engineering team reported meaningfully higher confidence shipping smaller, more frequent changes
- Feature lead time (code complete to live in production) dropped from up to two weeks to same-day in most cases
Technology Stack
| Layer | Technology |
|---|---|
| CI/CD | GitHub Actions |
| Containerization | Docker, Amazon ECR |
| Container Orchestration | AWS ECS |
| Infrastructure as Code | Terraform |
| Testing | Jest, Cypress |
| Code Quality | SonarQube |
| Monitoring | AWS CloudWatch |
Why the Solution Worked
The pipeline succeeded because automation was introduced in the right order PR-level testing and quality gates first, then staging automation, then production. This built the team’s trust in the system incrementally rather than asking them to hand over production deployment to automation on day one.
Automated rollback tied to real health checks, not just deployment success, was the safety net that made frequent deployment genuinely low-risk rather than just faster. That combination speed plus a real safety mechanism is what let the team confidently move to a continuous delivery model.
Future Scalability
The pipeline architecture is built to extend as the client’s engineering organization grows. Planned next steps include:
- Adding feature-flag-based canary releases for higher-risk changes
- Extending automated testing to include load and performance testing in the pipeline
- Building a self-service environment provisioning system so engineers can spin up isolated test environments per feature branch
- Applying the same pipeline pattern to additional services as the team splits the monolith into microservices
Because the pipeline is defined as versioned configuration (GitHub Actions workflows and Terraform), extending it to new services or environments doesn’t require rebuilding the underlying automation.
Final Outcome
The client moved from a slow, manual, high-risk release process to a fast, automated, and genuinely safer one increasing release frequency by an order of magnitude while reducing the time and stress associated with shipping. Engineering velocity is no longer bottlenecked by the deployment process itself.
