Do we need to deploy every day?

No. The point is being able to, not being obliged to. Some clients release several times a day, others once a fortnight on a fixed schedule. What matters is that an urgent fix can go out in twenty minutes when you need it to rather than next cycle.

Which CI tool should we use?

Usually the one attached to where your code already is, since the integration saves more than any feature comparison wins. GitHub Actions, GitLab CI, and Azure DevOps are all capable. Switching tools is rarely the fix for a pipeline problem.

What if our tests are unreliable?

Then that gets fixed first, because a pipeline built on tests that fail randomly teaches everyone to ignore failures. We identify the unreliable ones, repair or remove them, and only then make the pipeline something that can block a release.

Can we do this with a regulated approval process?

Yes, and automation usually improves the evidence. Approvals, separation of duties, and change records become enforced steps with timestamps rather than a process people are asked to follow, which is generally easier to defend in an audit.

CI/CD Pipelines
Development Services

Stateside Digitals builds CI/CD pipelines that test every change before it merges and put releases into production without anyone holding their breath, so shipping stops being something the team schedules around.

CI/CD is the automation between a developer finishing a change and that change reaching your users. Continuous integration builds and tests every change as it arrives. Continuous delivery takes whatever passes and moves it through your environments toward production, with as many or as few human approvals as your business actually requires.

For a business, this is the least abstract infrastructure you will ever buy. It comes down to two numbers you already feel without measuring them: how long it takes to get a fix in front of customers, and how often a release causes a problem. Everything else on this page exists to move those two numbers in the right direction.

Every Change Tested

Before Anyone Merges It

Problems are found while the person who caused them still remembers the change, which is when they are cheapest to understand and fix.

Releases Stop Being Events

No More Friday Freezes

Deploying becomes routine enough to happen mid-afternoon on a Thursday without anyone clearing their calendar to supervise it.

Mistakes Caught Early

Cheaper Than Caught Late

A failing test costs minutes, the same defect found by a customer costs a support queue, a hotfix, and some amount of goodwill.

First Pipeline Setup

Teams still deploying by hand, moving to automated builds, tests, and releases without pausing the work already in progress.

Slow Pipeline Rescue

Builds that grew to forty minutes and tests nobody trusts any more, brought back to something fast and reliable enough that developers stop working around it entirely.

Release Compliance

Approval gates, audit trails, and separation of duties built into the pipeline, so controls are evidenced rather than described.

The intuition points the other way. Deploying less often feels more careful, so teams batch their work into a monthly release with a long testing period in front of it. What arrives at the end is a release containing two hundred changes, and when something breaks the first job is working out which of the two hundred was responsible for it.

Teams that deploy frequently consistently report fewer failed releases rather than more, and the reason is arithmetic rather than culture. A deployment carrying one change is simple to diagnose and simple to reverse. Automation is what makes small releases cheap enough to be worth doing, and small releases are what make the whole arrangement safer than the careful version.

We build pipelines in GitHub Actions, GitLab CI, or Azure DevOps, whichever sits closest to where your code already lives. That covers build caching and parallel test runs to keep them quick, artifact registries, environment promotion with approvals where you need them, secrets handled outside configuration, and blue-green or canary releases with automated rollback.

CI/CD
GitHub Actions
GitLab CI
Azure DevOps
Build Caching
Parallel Tests
Artifact Registry
Environment Promotion
Secrets Management
Blue-Green
Canary Releases
Automated Rollback

Pipelines Under Ten Minutes

Secrets Never In Config

Rollback Is One Step

Flaky Tests Fixed, Not Retried

Caching and parallel runs keep the wait short, because a pipeline developers have time to context-switch away from is one they stop watching.

Credentials come from a secret store at run time, so nothing sensitive sits in a repository or a build log where it outlives the change that added it.

Reverting a release is a single action with a known outcome, which is what makes deploying on a Thursday afternoon a reasonable thing to do.

A test that fails at random gets repaired or removed, because retrying it teaches the whole team that red does not mean anything.

Pipelines Under Ten Minutes

Secrets Never In Config

Rollback Is One Step

Flaky Tests Fixed, Not Retried

A working pipeline for a single application usually takes two to three weeks, including the tests worth having before you automate anything. Larger estates take longer, though we would put one service through end to end first rather than planning it all.

CI/CD Pipelines
Development Services