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CASE STUDY
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Deploys so boring, you ship
Friday afternoon.

We containerize the app that only works on one laptop, then build the pipeline around it, build, test, scan, deploy, so every release is reproducible, gated and reversible. Zero-downtime rollouts with automatic rollback, on a platform sized to your team, not to a conference talk.
11 min
median commit-to-production on pipelines we run
140+
apps containerized and shipped through CI/CD
99.6%
change success rate across client deploys last year
Release pipeline
main · build #482
DEPLOYING
Docker
Build
Image pushed to registry
2m 04s
GitHub Actions
Test
612 passed, 0 flaky
4m 12s
Kubernetes
Staging
Smoke checks green
1m 30s
aws
Production
Rolling 3 / 6 nodes
live
GUARDSzero_downtime · auto_rollback · signed_images
14
deploys per week
8 min
commit to prod
0.4%
change failure
Teams deploying with Encloud
StratosNORTHBRIDGEmeridianBLUESUMMITCaremont

Shipping monthly, with fear:
where delivery breaks.

Four patterns show up in almost every delivery audit we run. Each one is a Dockerfile, a pipeline stage or a rollback rule away from fixed.
01SNOWFLAKE
The app only works on Dave’s machine
The build depends on a laptop, a folder of undocumented steps and one engineer’s memory. New hires lose their first week to setup, and every "works for me" bug costs a day of archaeology instead of a diff between two images.
02FEAR
Releases are events, so they happen monthly
Deploys mean a maintenance window, a checklist in a wiki and someone SSH-ed in at 11pm. Because each release is risky, you batch changes, and the bigger the batch, the riskier the release. Fear compounds itself.
03DRIFT
Staging passed. Production didn’t.
Dev, staging and prod run different OS versions, env vars and library patches, configured by hand, drifted for years. Tests certify an environment that no longer resembles the one your customers hit.
04SECRETS
Credentials live in .env files and Slack threads
Database passwords in the repo history, API keys pasted between teammates, nothing rotated since the person who set it up left. One leaked laptop or one disgruntled exit and every system is exposed at once.

Our fix: make every release a non-event.

Continuous delivery isn’t a tool you install, it’s a property you engineer: reproducible builds, one gated road to production, and rollouts that reverse themselves. We build that property into your stack, then prove it with your own DORA numbers.
Pressure-test your pipeline →
01
Containerize until "works on my machine" means every machine
Multi-stage Dockerfiles, pinned base images and a compose file that boots the whole stack on any laptop in minutes. The image that passed CI is byte-for-byte the image that runs in production, no rebuild, no drift.
Multi-stage buildsPinned imagesOne-command local stack
02
One road to production, with gates
Every change travels the same pipeline: build, unit and E2E tests, dependency and image scans, then deploy. Branch protection makes the pipeline the only way in, no SSH hotfixes, no artifacts built on laptops.
Build-test-scan-deployBranch protectionArtifact promotion
03
Deploy without downtime, roll back without drama
Rolling and blue-green rollouts behind health checks, with automatic rollback the moment error rates or checks go red. A failed deploy becomes a Sentry note to read Monday, not a war room on Saturday.
Blue-green & rollingHealth checksAuto-rollback
04
Right-size the platform, most teams don’t need Kubernetes
We run K8s daily and still recommend against it more often than for it. ECS, plain Compose on a VM, or Kubernetes when scale genuinely demands it, chosen from your traffic and team size, written up in Terraform either way.
ECS / Compose / K8sTerraform IaCHonest platform call

Docker & CI/CD services, end to end

All Cloud & DevOps services
01
App Containerization
Multi-stage DockerfilesImage slimmingLocal compose stackLegacy app packaging
02
CI Pipeline Design
GitHub Actions / GitLabBuild cachingPR checksDependency scanning
03
Zero-Downtime Deployment
Blue-green cutoverRolling updatesAuto-rollback rulesDeploy windows retired
04
Environment Parity
Terraform modulesConfig as codePreview environmentsDrift detection
05
Secrets Management
Vault / Secrets ManagerRotation policiesRepo history scrubLeast-privilege access
06
Test Automation Gates
Unit & E2E gatesFlaky-test quarantineCoverage thresholdsSmoke tests post-deploy
07
Orchestration & Platform
ECS & ComposeKubernetes when earnedNginx & load balancingCapacity planning
08
Delivery Metrics & DORA
DORA dashboardLead-time trackingFailure-rate alertsQuarterly targets

How a delivery pipeline ships

Five stages, each with named deliverables. Hover a stage to see what you get.
01
/ 05
Audit
01Map how a change reaches production today
We trace one real commit from laptop to customer, every manual step, every wait, every snowflake server, and baseline your deploy frequency, lead time and change-failure rate so improvement is measurable, not vibes.
Delivery mapDORA baselineRisk & quick-wins list
02Package the app to run anywhere
Multi-stage Dockerfiles per service, a compose file that boots the full stack locally, and images slimmed and pinned. New engineers go from git clone to running app in minutes, and CI builds exactly what prod runs.
Dockerfiles & composeImage registry setupLocal dev guide
03Build the gated road to production
CI stages for build, unit and E2E tests, dependency and image scans, then deploy, with secrets pulled from Vault at runtime, never baked in. Branch protection makes the pipeline the only path a change can take.
CI/CD workflowsTest & scan gatesSecrets integration
04First zero-downtime deploy, rehearsed
Blue-green or rolling rollout wired to health checks and auto-rollback, rehearsed on staging until it’s dull, including a deliberately broken release, so the whole team has watched a rollback succeed before go-live.
Rollout & rollback runbookRollback drill logDeploy checklist retired
05Raise the tempo, watch the numbers
With the safety net in place, release cadence goes from monthly to weekly to on-merge. A DORA dashboard tracks frequency, lead time and failure rate monthly, and we coach your team until the pipeline is theirs.
DORA dashboardCadence planTeam handover sessions

Delivery outcomes in spotlight

All case studies
From a monthly release train to 34 deploys a month
B2B SaaSCI/CD Pipelines
Stratos
34/modeploys, up from 1 release train
Client portrait
We used to plan releases like moon launches. Now a merged PR is in production by lunch, and nobody watches it happen.
Nate Brogan
CTO, Stratos
A bad release rolled itself back in 84 seconds
LogisticsZero-Downtime Deploys
NORTHBRIDGE
84 secauto-rollback, zero tickets raised
Client portrait
Health checks caught it, traffic swung back, and the on-call read about it in Sentry the next morning. Customers never noticed.
Igor Petrov
Head of Platform, Northbridge Logistics
New-engineer setup cut from three days to 25 minutes
ManufacturingContainerization
meridian
25 minfrom git clone to running stack
Client portrait
Contractors used to bill us three days before writing a line of code. Now the whole system boots with one command.
Daniel Okafor
COO, Meridian Manufacturing
Release controls passed the security audit with zero findings
HealthcareSecrets & Compliance
Caremont
0audit findings on release controls
Client portrait
Every credential in Vault, every deploy traceable to a reviewed PR. The auditors asked who set it up, that was a first.
Colin Mbeki
IT Director, Caremont Health
Failed changes cut from 1 in 7 to 1 in 50 with test gates
Financial ServicesTest Automation Gates
BLUESUMMIT
1.9%change-failure rate, from 14%
Client portrait
Risk used to review every release by hand. The pipeline now enforces more than our checklist ever did, and it never gets tired.
James Whitaker
Director of Risk, BlueSummit

Put a senior delivery pod on your pipeline, not a ticket queue.

A DevOps engineer, a platform architect and a delivery lead working in your repos from week one. The same pod containerizes, builds the pipeline and stays until your team runs it without us.
3 wks
Typical time from kickoff to first fully automated deploy
Median deploy-frequency gain in the first quarter
<90 s
Typical rollback on pipelines we put into production

The stack your releases run on

Boring, proven delivery tooling, chosen for your team’s size, not for our résumés.
Containers & runtime
Pipelines & IaC
Testing & quality gates
Secrets & configuration
Observability & data
The packaging and runtime layer that makes every environment identical.
DockerDocker
KubernetesKubernetes
NGINXNginx
awsAWS ECS

Book a pipeline audit, not a sales call.

45 minutes with a DevOps engineer. Bring repo access or just a description of release day, leave with a delivery map, your rough DORA baseline, and an honest read on whether you need Kubernetes or just a good pipeline.
No obligation, no prepared pitch
NDA on request before you share access
Platform-honest, we’ll talk you out of K8s if you don’t need it
4.9 / 5average across 80+ delivery engagements
They watched one release and found nine manual steps we’d stopped noticing. Six weeks later the whole path was a pipeline, and deploys stopped being meetings.
Nate Brogan
CTO, Stratos
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Frequently asked questions

Weighing Kubernetes against something simpler, or wondering what a pipeline changes day to day? Bring the question to a pipeline audit and get an answer backed by your own delivery data.
Talk to a DevOps engineer →
How long does it take to containerize our app and get a first pipeline live?+
For a typical web app, Docker containerization plus a first CI/CD pipeline, build, tests, scans, automated deploy to staging, lands in two to three weeks. The first zero-downtime production deploy usually follows within week four, after a rehearsed rollback drill. Sprawling legacy monoliths take longer; the audit gives you a real timeline, not a guess.
Do we actually need Kubernetes?+
Probably not, and we say that as a team that runs it daily. Kubernetes earns its operating cost when you have many services, spiky scale or a platform team to feed it; most businesses ship faster on ECS or plain Docker Compose behind Nginx, defined in Terraform. We put the recommendation in writing with the math, and the pipeline is built so you can move to K8s later without starting over.
How do rollbacks work when a deploy goes wrong?+
Every rollout runs behind health checks and error-rate thresholds: blue-green deploys swing traffic back to the previous version, rolling deploys halt and revert to the last good image. Because images are versioned artifacts, rollback is a redeploy of a known-good build, typically under 90 seconds, automatic, no SSH required. We rehearse it with a deliberately broken release before go-live.
Our test suite is flaky. Won’t gates just block every deploy?+
Not if flakiness is treated as a defect instead of weather. We quarantine flaky tests into a non-blocking lane with an owner and a fix-by date, keep the blocking gate green and trustworthy, and burn the quarantine list down week by week. A gate the team routinely overrides is worse than no gate, so we never ship one the suite can’t honor.
How do you handle secrets and credentials in the pipeline?+
Secrets move into Vault or AWS Secrets Manager and are injected at deploy time, never baked into images, committed to the repo or pasted in chat. We scrub leaked credentials from git history, rotate everything that was exposed, and scope access per environment so staging keys can’t touch production data.
What changes for our developers day to day?+
Less than they fear, and mostly for the better: they open PRs exactly as before, but checks now run automatically, merge means deployed, and local setup is one docker compose up instead of a wiki page. What disappears is the bad part, release-day checklists, SSH hotfixes and the deploy rota. We run handover sessions until the pipeline feels like theirs, not ours.
What does a Docker and CI/CD engagement cost?+
The pipeline audit is fixed-price. Containerization plus a full build-test-scan-deploy pipeline is scoped after the audit and quoted as a fixed project, most land in the four-to-eight-week range depending on service count and test coverage. Ongoing platform support is an optional monthly retainer you can stop anytime; either way, the pipelines and Terraform are yours.
Can you work with our existing GitHub, GitLab and AWS setup?+
Yes, we build inside your accounts and repos, not a parallel universe. GitHub Actions or GitLab CI on whichever platform you already pay for, deploying to your AWS. Nothing is locked to us: every workflow, Dockerfile and Terraform module lives in your repo, documented, from day one.

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