DevOps platform engineering, automation pipelines and infrastructure as code

Service Capability

DevOps & Platform Engineering

CI/CD pipeline design, Kubernetes platform operations, infrastructure as code, and observability engineering that increases delivery velocity and operational reliability for engineering teams.

What we do

Platform engineering is about removing friction from software delivery. When every team is building and maintaining their own CI/CD pipelines, Kubernetes configurations, and monitoring stacks, the result is duplicated effort, inconsistent practices, and engineers spending time on infrastructure instead of product features.

We build and operate the internal platforms that allow your engineering teams to deploy, scale, and observe their services without becoming Kubernetes experts. This means standardised pipelines, self-service infrastructure, opinionated defaults, and observability that gives teams the signal they need to understand what their services are doing in production.

We treat the platform as a product — with internal engineering teams as the customers — and measure success by how fast those teams can get code from commit to production with confidence.

Problems we address

  • — Release cycles are slow because deployments are manual, require specialist knowledge, or fail unpredictably
  • — Environments are inconsistent — what works in staging breaks in production for reasons nobody can explain
  • — There is no meaningful observability — incidents are discovered by customers before alerts fire
  • — Infrastructure is provisioned manually and inconsistently, leading to configuration drift and security risks
  • — Kubernetes clusters have grown organically without governance, cost controls, or operational runbooks
  • — Security scanning and secrets management are absent from pipelines, leaving vulnerabilities undetected until too late

Capabilities

CI/CD Pipeline Design & Standardisation

Standardised, reusable pipeline templates for build, test, and deployment using GitHub Actions, GitLab CI, or Jenkins. Consistent patterns across teams with shared library components and quality gates.

Kubernetes Cluster Design & Operations

Production-grade cluster architecture for EKS, AKS, or GKE — namespace strategy, RBAC, network policies, resource quotas, autoscaling, and pod disruption budgets designed for multi-team environments.

Infrastructure as Code

All infrastructure defined in Terraform or Pulumi with modular, reusable components. State management, secret injection, drift detection, and CI-driven plan/apply workflows to eliminate manual provisioning.

Internal Developer Platform (IDP)

Self-service platform capabilities using Backstage or equivalent — service catalogues, scaffolding templates, environment provisioning, and standardised deployment workflows that reduce the cognitive load on product engineers.

Observability Stack

Metrics (Prometheus/Grafana), structured logs (Loki/ELK), and distributed tracing (Jaeger/Tempo) deployed as an integrated stack with service-level objective dashboards, alerting runbooks, and on-call tooling.

GitOps & Security in Pipelines

GitOps deployments with ArgoCD or Flux for declarative, auditable cluster state. Pipeline security gates including SAST scanning, dependency vulnerability checks, secret scanning, and container image signing.

Our approach

Platform as a product

We treat the internal platform as a product with engineering teams as users. That means understanding their pain points, measuring adoption and friction, and building capabilities that actually reduce their toil — not capabilities that look good on a slide but add complexity to their daily work.

Pave the path, don't mandate the route

The best internal platforms make the right thing the easy thing. We build opinionated defaults that work out of the box, with escape hatches for teams that have legitimate reasons to deviate — rather than enforcing standards through policy documents that engineers route around.

Observability before optimisation

You can't improve what you can't measure. We establish meaningful observability — metrics aligned to SLOs, structured logs that support incident investigation, and traces that show distributed call paths — before recommending any performance or reliability improvements.

Security is a pipeline requirement, not an audit outcome

Vulnerabilities caught in the pipeline cost orders of magnitude less to fix than those caught in production. We integrate automated security scanning as a standard pipeline step so that security feedback is fast, consistent, and doesn't depend on a separate team reviewing every deployment.

Delivery process

  1. 1

    Platform & pipeline audit

    Assess existing CI/CD, infrastructure, observability, and deployment practices. Identify the highest-friction points for engineering teams and the most significant reliability or security risks.

  2. 2

    Prioritise bottlenecks

    Work with engineering leadership to sequence improvements by impact and effort. Agree a roadmap that delivers quick wins within weeks while building toward a fully standardised platform.

  3. 3

    Pipeline standardisation

    Build shared pipeline templates and migrate existing pipelines team by team. Establish quality gates, security checks, and deployment approval workflows.

  4. 4

    Platform layer & IDP

    Build Kubernetes infrastructure, GitOps configuration, and self-service capabilities. Deploy IDP if scope includes developer portal for service discovery and scaffolding.

  5. 5

    Observability & continuous improvement

    Deploy full observability stack, define SLOs with engineering teams, and establish a continuous improvement cadence with platform metrics tracked over time.

Technologies

Container & Orchestration

KubernetesHelmDockerIstio

GitOps

ArgoCDFlux

CI/CD

GitHub ActionsGitLab CIJenkinsTekton

Infrastructure as Code

TerraformPulumiAWS CDKAnsible

Observability

PrometheusGrafanaLokiJaegerOpenTelemetry

Security & Secrets

HashiCorp Vaultcert-managerTrivySnyk

Industries served

Financial Services Healthcare Retail Manufacturing Technology Logistics

Related case studies

Financial Services

Banking Platform Modernization

End-to-end platform engineering for a banking microservices migration — including Kubernetes on EKS, ArgoCD GitOps, Terraform IaC, and full Prometheus/Grafana observability stack.

Frequently asked questions

Kubernetes is the right choice when you need to run multiple services, have multiple engineering teams deploying independently, require fine-grained scaling, or are on a cloud provider where managed Kubernetes (EKS, AKS, GKE) reduces the operational cost significantly. For a small number of services with a single team, simpler alternatives — container services, serverless, or even well-configured VMs — may be appropriate and considerably cheaper to operate. We make this assessment based on your actual workload profile, not a default recommendation toward the most complex option.
Jenkins migration is worth pursuing when Jenkins is becoming a maintenance burden, pipelines are heavily customised in Groovy that nobody wants to maintain, or you're already using GitHub or GitLab for source control and want tighter integration. Modern CI platforms offer better developer experience, native secrets management, and lower operational overhead than a self-hosted Jenkins instance. However, migration requires investment to rewrite pipelines — we assess whether the ongoing savings justify the switching cost before recommending migration.
A platform team serving 50–100 engineers typically needs three to six full-time engineers depending on the breadth of the platform scope. Below 30 engineers, a dedicated platform team is often premature — shared DevOps practices and standardised tooling choices can deliver most of the value. The key signal for investing in a platform team is when the cognitive overhead of infrastructure is measurably slowing product delivery across multiple teams — not when it's conceptually appealing. We help organisations assess readiness for platform team investment and build the capability incrementally.
Monorepos simplify cross-service refactoring, dependency management, and shared tooling configuration. Polyrepos give teams stronger autonomy and simpler per-repository CI configuration. The right choice depends on your team structure and deployment independence requirements. If teams share a lot of code and deploy together frequently, a monorepo with affected-change detection (Nx, Bazel, or Turborepo) is often the better choice. If teams deploy independently and share little code, polyrepo with shared pipeline templates works well. We're experienced with both and recommend based on your actual team topology.
Monitoring tells you when something is wrong based on conditions you anticipated in advance — thresholds, health checks, known failure modes. Observability lets you understand the internal state of a system from its external outputs (metrics, logs, traces), including for conditions you didn't anticipate when setting up the monitoring. In practice, you need both: monitoring for alerting on known failure patterns, and observability tooling — particularly distributed tracing — for investigating novel incidents in complex distributed systems. A system with only dashboards and no structured logs or traces will leave you blind during the incidents that matter most.

Accelerate your engineering delivery

Whether you need to modernise your CI/CD, build a Kubernetes platform, or establish meaningful observability, our platform engineering team can help you move faster with more confidence.

Talk to our platform team View case studies