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DevOps & Cloud Engineering

Cloud platforms that don't surprise you on the invoice or the incident page.

Cloud foundations, platform engineering, and SRE for organisations that take uptime and unit economics seriously. AWS, Azure, GCP, and sovereign-cloud, with FinOps and AI-augmented operations baked in.

Where this goes wrong

The patterns we are most often called in to fix.

Pattern 01

Cloud spend that grows faster than revenue with no clear owner.

Pattern 02

Incidents that the team learns about from customers first.

Pattern 03

Compliance teams blocking releases because controls aren't automated.

Our process

How we deliver: Discover, Architect, Build, Run

  1. 01

    Discover

    Frame the problem with the people closest to it. Map the system. Surface the assumptions and the unknowns.

  2. 02

    Architect

    Design the target state and the path to it. Make the trade-offs explicit. Earn buy-in across engineering, security, and the business.

  3. 03

    Build

    Cross-functional delivery with a definition of done that includes observability, security, and runbooks, not just feature acceptance.

  4. 04

    Run

    Operate with you. Continuous improvement against business outcomes, with FinOps and AI-augmented DevOps baked in.

What’s in scope

The capabilities you draw on across an engagement.

  • Landing zones and multi-account / subscription design
  • Infrastructure as Code (Terraform, Pulumi, Bicep)
  • Kubernetes platform engineering and internal developer platforms
  • CI/CD pipelines, progressive delivery, supply-chain security
  • FinOps: cost visibility, anomaly detection, optimisation agents
  • SRE: SLOs, error budgets, incident tooling, chaos engineering
  • Sovereign and on-premise deployments for regulated workloads

Technology footprint

We are pragmatic about technology, and AI-agnostic by default.

AWSAzureGoogle CloudKubernetesTerraformArgoCDDatadogGrafanaOpenTelemetry
Anonymised case study · Cross-industry

AI-augmented DevOps for a regulated enterprise

DevOps AI agent that triages incidents, proposes runbook actions, and augments PR review across a multi-cluster Kubernetes estate.

−74%
Mean time to detect
+38%
Incidents resolved within SLA
−46%
PR review cycle time
Read case study

Related services

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