Ship faster without your pipeline becoming the thing that breaks
We test every layer of your delivery pipeline — CI/CD workflows, Infrastructure as Code, container orchestration, and AI-driven observability — so a bad deploy fails a quality gate instead of paging someone at 2am.
- US, Canada & UK overlapWorking hours planned around your team, not just ours.
- One point of contactA named lead owns the engagement end to end — no ticket queue.
- NDA on every engagementSigned before we see a single ticket or line of test data.
- Scale up or down monthlyNo multi-year lock-in — resize the team as the release calendar changes.
| Pipeline layer | What we validate | Tools & techniques | Failure it catches |
|---|---|---|---|
| CI/CD Pipeline | Build triggers, quality gates, artifact integrity, failure handling | Jenkins, GitHub Actions, GitLab CI, Azure DevOps | Silent pipeline failures, skipped quality gates |
| Infrastructure as Code | Terraform, CloudFormation, or Ansible policy compliance | tfsec, Checkov, static analysis | Misconfigured infrastructure reaching production |
| Containers & Kubernetes | Image vulnerabilities, Helm chart correctness, autoscaling behaviour | Image scanning, Helm lint, load-driven autoscale tests | Vulnerable images, pods that don’t scale under load |
| Chaos & Resilience | Failover, self-healing, blast radius containment | Controlled fault injection — service kill, network latency, resource exhaustion | Unverified "high availability" that fails under a real fault |
| Release Strategy | Canary / blue-green traffic shift, rollback triggers | Progressive delivery tooling, automated rollback tests | A bad release reaching 100% of users before anyone notices |
| AIOps & Observability | Alert correlation accuracy, anomaly-detection logic, dashboards | Prometheus, Grafana, Datadog, Dynatrace validation | Alert fatigue, false positives, and missed incidents |
Who this is for
What you get
- A pipeline and infrastructure audit identifying coverage gaps, flaky stages, and reliability risks across your CI/CD and IaC setup
- 15-25 automated checks embedded into your pipeline (Jenkins, GitHub Actions, GitLab CI, or Azure DevOps) covering quality gates, artifact integrity, and failure handling
- IaC validation (Terraform, CloudFormation, or Ansible) using static analysis and policy-as-code tools such as tfsec or Checkov
- Container/Kubernetes checks covering image vulnerability scanning, Helm chart validation, and autoscaling behaviour
- One controlled chaos engineering experiment (service kill, network latency, or resource exhaustion) validating failover and self-healing
- Deployment strategy validation for canary, blue-green, or rolling releases, including rollback trigger testing
- A findings report mapped to DORA metrics (deployment frequency, lead time, change failure rate, MTTR) plus a handover session
Timeline & process
Pipeline & infrastructure assessment
We audit your CI/CD pipelines, IaC repositories, container setup, and monitoring stack to identify coverage gaps and reliability risks.
Test strategy & tooling integration
We define a test strategy mapped to your delivery cadence and integrate pipeline testers, IaC scanners, and chaos tooling.
Embedding & chaos validation
Quality gates and policy checks are embedded into your pipeline, and we run the agreed chaos experiment against a staging or production-like environment.
Reporting & handover
We deliver the findings report against DORA metrics, walk your team through results, and hand over everything we built.
What we need from you
- Read access to your CI/CD pipeline configuration and IaC repositories
- A staging or production-like environment where a chaos experiment can safely run
- Access to your observability/AIOps stack (e.g. Prometheus, Datadog, Dynatrace) for alert and dashboard validation
- One walkthrough call with your platform or SRE team before we start
Proof, not promises
Not in scope, so there are no surprises
- Building or re-architecting your CI/CD pipeline or IaC codebase — we test what exists, your team implements fixes
- Full security penetration testing of infrastructure — IaC and container scans catch common misconfigurations, this isn't a pentest
- AIOps model training or building new anomaly-detection models — we validate accuracy and alert logic against what you already run
- Chaos experiments against production without an agreed game-day plan and rollback safety net
- Ongoing pipeline monitoring after the engagement — available separately as a retainer
Questions we get asked
What is AIOps testing and why does our observability stack need it?
AIOps testing validates the AI-driven layer of your observability stack — alert correlation rules, anomaly-detection models, and auto-remediation logic — against known-good and known-bad scenarios. Without it, teams only find out an alert rule is wrong when it either misses a real incident or cries wolf often enough that on-call starts ignoring it.
Is a chaos engineering experiment safe to run against our environment?
Only with an agreed game-day plan: a defined blast radius, a rollback safety net, and sign-off from whoever owns the environment. We run the first experiment against staging or a production-like environment by default, and only move to production once failover behaviour is proven and your team is comfortable with the trigger.
Do you test the CI/CD pipeline itself, or just what runs inside it?
Both. We validate the pipeline's own behaviour — quality gates actually blocking on failure, artifact integrity, failure handling — in addition to the IaC, container, and deployment checks that run inside it. A green pipeline that skips a gate silently is as risky as a bug the gate was supposed to catch.
Can you work with our existing CI/CD and observability tools, or do we need to switch?
We work inside what you already run — Jenkins, GitHub Actions, GitLab CI, or Azure DevOps for pipelines, and Prometheus, Grafana, Datadog, or Dynatrace for observability. The engagement embeds checks into your existing setup rather than asking you to adopt new tooling.
How do you map findings to DORA metrics?
The findings report ties specific issues to the DORA metric they affect — a flaky quality gate to change failure rate, a slow rollback trigger to MTTR, an untested canary step to deployment frequency — so the business impact of each gap is clear, not just a list of technical findings.