CI/CD Consulting & Pipeline Automation
Ship faster, break less. We design and implement CI/CD pipelines that let your team release multiple times per day without manual steps, without fear, without "the one person who knows how." As AI agents write more of your code, we also wire AI-assisted review into the pipeline - with the guardrails that keep auto-merge safe.
Sound familiar?
Deploys require a specific person
One engineer holds the keys to production. When they're on leave, nothing ships.
Builds take 15+ minutes
PRs queue up. Developers context-switch. Feedback that should take seconds stretches to hours.
Friday releases terrify the team
"Let's wait until Monday" is your unofficial deployment policy.
PRs pile up faster than you can review them
AI agents open more pull requests than your team can read. Review is now the bottleneck, not writing code.
No staging environment
Code goes from a laptop to production with nothing in between.
Secrets live in plaintext
API keys in Git, credentials in Slack, tokens in shared docs.
Rollback means "revert and pray"
No blue-green. No canary. No automated recovery when things go wrong.
What changes after we're done
Multiple deploys per day
Automated pipelines mean any team member can ship to production in minutes, not days.
90% fewer deployment failures
Automated tests, security scans, and environment promotion catch problems before users do.
Hours back per sprint
No more manual release rituals. Your engineers build product instead of babysitting deployments.
Audit trail for every change
Who deployed what, when, and why. Full traceability for compliance and incident response.
How we solve deployment problems
Problem
Deploys require a specific person
We automate the entire path from commit to production. Any team member can trigger a release.
Problem
Builds take 15+ minutes
We parallelise jobs, add layer caching, split test suites, and eliminate redundant steps.
Problem
No staging environment
We build environment promotion pipelines - dev, staging, production - with automated gates between each.
Problem
Secrets in plaintext
We implement OIDC authentication, vault integration, and scoped secrets with zero long-lived credentials.
Problem
No rollback strategy
Blue-green or canary deployments with automated rollback triggered by error rate thresholds.
Problem
No deployment audit trail
Every deploy is logged, attributed, and linked to the triggering commit and approval.
Problem
PRs pile up faster than you can review them
We add AI code review as a PR gate with blast-radius triage - low-risk changes ship on the AI review, high-risk ones (auth, public APIs, schema) still require a human - backed by tests, scans, and rollback that make it safe.
What we implement
Automated testing and builds
Unit, integration, and E2E tests triggered on every push. Quality gates that block broken code from progressing.
Terraform pipelines
Plan on PR, apply on merge. No manual terraform apply from anyone's laptop. State locking and drift detection included.
Container build and deploy
Multi-stage Docker builds with caching, vulnerability scanning, image signing, and push to ECR or your registry.
Deployment strategies
Blue-green, canary, and rolling deploys to ECS, EKS, or Lambda with automated rollback on failure.
GitOps with ArgoCD
Declarative Kubernetes delivery with automated sync, drift reconciliation, and multi-cluster support.
Pipeline security
SAST/DAST scanning, dependency checks, OIDC auth, and least-privilege runners built into every pipeline.
AI code review in the pipeline
AI review wired in as a PR gate with blast-radius triage and human-in-the-loop for high-risk changes - integrated into GitHub Actions or Azure DevOps, backed by the tests, scans, and rollback that make auto-merge safe.
The engagement
1
Week 1: Audit
We map your current deployment process, measure build times and failure rates, and identify the highest-impact improvements.
2
Week 2: Design
We architect the pipeline stages, branching strategy, environment promotion, and security model. You review before we build.
3
Weeks 2-3: Build
We implement pipelines in your repository as code. Automated tests, deploys, rollbacks, secrets - all committed and working.
4
Week 4: Handover
Documentation, runbooks, and knowledge transfer sessions. Your team owns it. We provide a support window for questions.
Ready to ship with confidence?
Who trusts us
"Devopsity has delivered a high-quality CI/CD performance improvements in our delivery pipelines. They implemented solutions that we still use to facilitate further app developments. Their team was committed to the project and customised their initial plan and offering to accommodate our budget."
Łukasz Królak
Head of Product Development, ZIPZERO Global LTD
"Devopsity helped us improve deployment efficiency and reduce costs for running deployments and cloud services. We appreciated how they helped us meet our immediate goals on time."
Dave
Head of Engineering, Educational Game Developer, Edinburgh based EdTech
- ✓ AWS Advanced Tier Partner with DevOps Engineer Professional certification
- ✓ Senior engineers only - no juniors, no offshore handoffs
- ✓ Both application and infrastructure pipelines (Terraform included)
- ✓ Flexible engagements from 4-hour reviews to full sprints
Frequently asked questions
CI/CD Consulting: From Audit to Production-Ready Pipelines
Who needs CI/CD consulting?
Engineering teams that have outgrown manual deployments but lack the DevOps expertise to build automated pipelines in-house. Common triggers: scaling from 5 to 20+ developers, preparing for SOC2/ISO compliance, migrating from Jenkins to GitHub Actions, or adopting Kubernetes with GitOps.
CI/CD implementation vs CI/CD consulting
Implementation means we build your pipelines. Consulting means we design the architecture, review what exists, and guide your team to build it. Most engagements blend both: we design with you, build the critical path, and document the rest for your team to complete.
Tools we implement
| Use case | Primary tool | Alternatives |
|---|---|---|
| Application CI/CD | GitHub Actions | GitLab CI, Bitbucket Pipelines |
| Infrastructure CI/CD | Terraform + GitHub Actions | OpenTofu, Atlantis |
| Kubernetes delivery | ArgoCD | Flux, Helm + GitHub Actions |
| Security scanning | Trivy, Snyk | Grype, Dependabot |
| Secrets management | AWS Secrets Manager + OIDC | HashiCorp Vault |
CI/CD for regulated industries
We implement pipelines that satisfy SOC2, ISO 27001, and FCA requirements: signed artifacts, immutable audit logs, separation of duties, and automated compliance gates. Read our approach to UK fintech cloud compliance →
Continuous delivery consulting
Continuous delivery means every commit is deployable to production at any time. Our continuous delivery consulting focuses on the practices and tooling that make this possible: trunk-based development, feature flags, automated quality gates, and environment promotion strategies. We design systems where deploying is a non-event — predictable, repeatable, and safe.
CI/CD pipeline consulting for cloud-native teams
Cloud-native CI/CD differs from traditional approaches. Container builds, multi-environment Kubernetes deployments, infrastructure-as-code pipelines, and GitOps require different patterns than simple application deploys. We specialise in CI/CD for teams running on AWS (ECS, EKS, Lambda), Azure (AKS, Container Apps), and GCP (GKE, Cloud Run).
CI/CD automation services
Our CI/CD automation services eliminate manual steps from your delivery process: automated testing on every push, security scanning integrated into pipelines, infrastructure provisioning triggered by code changes, and deployment strategies that roll back automatically on failure. The result is a fully automated path from developer commit to production deployment.
AI code review in your CI/CD pipeline
As AI agents generate more of your code, review becomes the bottleneck. We wire AI code review in as a pull-request gate with blast-radius triage: low-risk changes ship on the AI review, while high-risk ones (authentication, public APIs, database schema) still require a human. The guardrails underneath are what matter - tests, scans, observability, and a working rollback - without them it is not automating review, just removing it. How to wire AI code review into your pipeline (and what it misses) →