Containerisation
Docker and Compose so staging and production run the same image. No more "it worked on my machine" and no more manual server configuration drift.
DevOps, Testing & Cloud
Deployments that are boring, reversible and take minutes — with automated tests as a gate, cloud hosting that is reproducible, and backups you have actually tested restoring.
What this covers
Most teams do not have a deployment process; they have a person who remembers the commands. That works until that person is on leave. We replace it with a repeatable pipeline: containerised builds, automated tests as a gate, one-command deploys to a cloud environment that matches your local one, and a restore drill that proves your backups work before you need them.
- One command to deploy, one command to roll back
- Automated tests gate every release, so bad builds never reach users
- A restore drill performed with you, not just a backup job that emails you
Capabilities
The specifics, rather than the adjectives.
CI/CD pipelines
GitHub Actions or GitLab CI: lint, build, test, then deploy on merge. A red pipeline blocks the release automatically instead of relying on someone noticing.
Zero-downtime releases
Health-checked rollouts that keep serving traffic during a deploy, with an automatic rollback when the new version fails its own health check.
Backups and restore drills
Automated database and file backups to separate storage, a documented retention policy, and a restore that we have actually run end-to-end and timed.
Monitoring and alerting
Uptime checks, error-rate tracking, disk and memory alerts, and a log destination you can search. Alerts that matter go to a channel someone reads, not to a mail folder nobody opens.
Secrets and access hygiene
Environment-based configuration, no credentials in Git, least-privilege deployment keys, HTTPS everywhere and an inventory of who can reach production.
Automated testing as a release gate
Pytest for backend logic and Playwright for the flows a user actually performs, wired into the pipeline so a failing test stops the release instead of being mentioned in a report nobody opens. Written around the rules that carry money or access, not to satisfy a coverage number.
Code quality and review setup
Formatters, linters and type checks running automatically, a branch and review process your team will actually follow, and a CI check that refuses to merge a broken build. The point is to keep review conversations about design rather than about a missing semicolon.
What you get
A concrete list of outputs. If something here is not relevant to your project, it comes out of the scope and the price — we would rather agree that in writing now.
- Dockerfiles and Compose configuration for each environment
- CI pipeline that lints, builds and tests on every push
- Automated test suite: Pytest for logic, Playwright for user flows
- Automated deployment to AWS or Azure, with health checks and rollback
- Server hardening: firewall rules, SSH key access, disabled password login
- Backup schedule, retention policy and an off-site copy
- Performed and timed restore drill, with a written runbook
- Monitoring dashboards and alert routing
- Code-quality tooling and a review process the team can follow
- Documented environment variables and secret-handling process
How it runs
Five steps. You always know which one you are in.
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01
Inventory
What runs where, who has access, what breaks most often, and how long a deploy currently takes. Measured, so improvement is provable.
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02
Containerise
Application and dependencies captured in images that run identically on your laptop and on the server.
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03
Automate
Pipeline, tests and deployment script. The first version is deliberately simple and readable — a clever pipeline nobody can debug is worse than no pipeline.
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04
Protect
Backups, restore drill, monitoring and alerts. This step is where most engagements find the genuinely dangerous gaps.
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05
Hand over
Runbooks for deploy, rollback, restore and incident response, plus a walkthrough with whoever will be on call.
Stack
The platforms and frameworks we build, integrate and maintain on. We would rather work in a stack your team can hire for and hand over than pick whatever is interesting this year — and where an existing platform does the job better than custom code, we will say so.
- Docker
- Docker Compose
- GitHub Actions
- GitLab CI
- Pytest
- Playwright
- AWS
- Azure
- Nginx
- Ubuntu Server
- Cloudflare
- Prometheus
- Grafana
- MySQL
- PostgreSQL
- Redis
FAQs
DevOps, Testing & Cloud — frequently asked questions
Is Docker worth it for a small project?
Below a certain size, no — and we say so. The value appears when there is more than one environment, more than one contributor, or a staging environment that has drifted away from production. Those are exactly the conditions under which "it worked on staging" becomes an outage.
We already have backups. Do we need a restore drill?
Yes, and it is the single most valuable hour in this engagement. Backups that have never been restored are a hypothesis. Common failures we have found: the dump includes tables but not stored routines, the off-site copy stopped syncing four months ago, or the restore takes longer than your recovery-time objective allows.
We cannot afford downtime for a deployment. Is that possible?
Yes, for most applications. The pattern is a health-checked rolling replacement: the new version starts, proves itself healthy on a real request, and only then receives traffic. A failed health check leaves the previous version serving.
Will this work on shared hosting, or do we need a VPS?
CI/CD and containerisation need a VPS or cloud instance — shared hosting does not give you the control they depend on. If you are on shared hosting and it is working well, a lighter version of this engagement is still useful: automated backups, a tested restore, uptime monitoring and a documented deployment checklist.
How do you handle our existing production data during setup?
Production is treated as read-only until a staging replica exists. We take a full backup before any change, rehearse the migration on staging, and cut over in a low-traffic window with a rollback point defined in advance.
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