DevOps & Generative AI services on AWS

A deployment should be a non-event. We rebuild your delivery path so every merge is automatically built, tested, scanned, signed and promoted through environments — with a rollback that takes one click and a history you can audit.

• Pipeline design for monorepos or many services, with reusable workflow templates
• Reproducible builds, dependency caching and parallel test matrices that cut build times
• Artefact registries with versioning, retention policy and promotion between environments
• Progressive delivery — blue/green, canary and feature flags with automated rollback triggers
• GitOps with Argo CD or Flux, so the cluster state always matches what is in Git
• DORA metrics instrumented from day one so improvement is measurable, not anecdotal

Typical stack: GitHub Actions, GitLab CI, Jenkins, Azure DevOps, Argo CD, Flux, Tekton, CircleCI.

Click-ops leaves you with an environment nobody can rebuild and a disaster recovery plan that is really a hope. We codify what you already run, split it into reviewed modules, and put a plan-and-approve workflow in front of every change.

• Import and codify existing infrastructure without a rebuild-from-scratch project
• Versioned, tested module libraries with sane defaults and documented inputs
• Multi-account landing zones, SSO, guardrails and organisation policy from the start
• Remote state with locking, encryption and audit history
• Drift detection and automated plan output posted on every pull request
• Ephemeral environments that spin up per branch and tear themselves down

Typical stack: Terraform, OpenTofu, Terragrunt, Pulumi, Ansible, Packer, Crossplane, CloudFormation.

Kubernetes rewards teams who set it up properly and punishes everyone else. We build clusters that are boring to operate: predictable upgrades, sensible autoscaling, network policy that is actually enforced, and a GitOps delivery model your developers can self-serve.

• Cluster architecture, node pools and a documented, rehearsed upgrade path
• Workload autoscaling with HPA, VPA and Karpenter or Cluster Autoscaler
• Helm charts and Kustomize overlays standardised across services
• Ingress, service mesh, mTLS and network policy where they earn their complexity
• Multi-tenancy with namespaces, quotas, RBAC and admission control
• Stateful workloads, backups and tested restore procedures

Typical stack: Kubernetes, EKS, AKS, GKE, Helm, Kustomize, Istio, Cilium, Karpenter, Argo Rollouts.

Migrations fail when they are treated as a lift-and-shift deadline instead of a sequence of reversible steps. We assess the estate, decide honestly what to rehost, replatform, refactor or retire, then move workloads in waves with a rollback available at each one.

• Discovery and dependency mapping across servers, databases and integrations
• A 6R decision per workload with cost and effort attached, not a blanket strategy
• Landing zone, networking, identity and guardrails built before the first workload moves
• Database migration with replication and a tested, timed cutover plan
• Wave-based migration with rollback criteria defined in advance
• Post-migration optimisation so the new bill is not the old bill plus a premium

Typical stack: AWS, Azure, Google Cloud, VMware, OpenStack, DMS, Velero, Cloudflare.

Security reviews at the end of a release are how deadlines slip. We move the controls into the pull request, the build and the admission controller, so risky changes are blocked automatically and audit evidence generates itself as a side effect of normal work.

• Secrets out of repositories and into Vault, SOPS or cloud KMS, with rotation
• SAST, SCA, DAST, IaC scanning and container CVE gates wired into every build
• SBOM generation, image signing and provenance attestation with Sigstore
• Policy as code with OPA or Kyverno enforced at admission across all clusters
• Least-privilege IAM, short-lived credentials and workload identity federation
• Continuous control evidence mapped to SOC 2, ISO 27001, HIPAA or PCI DSS

Typical stack: HashiCorp Vault, Trivy, Snyk, SonarQube, OPA, Kyverno, Cosign, Falco, AWS Security Hub.

Most teams have plenty of monitoring and very little observability: hundreds of alerts, none of which answer "is the customer affected, and why". We instrument what matters, define service level objectives with your product owners, and make the on-call rota survivable.

• OpenTelemetry instrumentation for metrics, logs and distributed traces
• SLIs and SLOs agreed with the business, with error budgets that drive decisions
• Alert rules rewritten around symptoms, so the pager means something
• Dashboards per service and per journey, not per server
• Incident process, severity model, comms templates and blameless postmortems
• Log volume and retention tuned so observability does not become the biggest bill

Typical stack: Prometheus, Grafana, Loki, Tempo, Mimir, OpenTelemetry, Datadog, Elastic, PagerDuty.

Cloud bills grow quietly: an oversized cluster here, a forgotten environment there, and a log pipeline nobody has looked at since it was built. We find the spend that nobody owns, cut it without touching performance, and leave a model that keeps it from creeping back.

• Full cost breakdown by service, team and environment — including the untagged remainder
• Rightsizing compute, storage tiering and lifecycle policies with measured impact
• Commitment planning: reserved instances, savings plans and spot where it is safe
• Autoscaling and scale-to-zero for non-production environments
• Tagging strategy enforced by policy, with per-team showback dashboards
• Anomaly alerts so an accidental instance is caught in hours, not at month end

Typical stack: AWS Cost Explorer, Azure Cost Management, GCP Billing, Kubecost, OpenCost, Infracost.

Once you have more than a handful of teams, every one of them reinvents the same pipeline slightly differently. A platform fixes that — not by adding a gatekeeper, but by making the paved road the fastest route to production.

• Golden path templates so a new service ships with CI, monitoring and alerts built in
• Self-service environment provisioning without raising a ticket
• A service catalogue with ownership, dependencies and on-call clearly recorded
• Developer portal built on Backstage, with scorecards for maturity and compliance
• Platform treated as a product: roadmap, users, feedback and adoption metrics

Typical stack: Backstage, Crossplane, Argo Workflows, Terraform, Kubernetes, GitHub.

If you would rather your engineers built product than patched nodes, we will take the pager. Named engineers who know your stack, an agreed SLA, and a monthly report that tells you what happened and what we are improving next.

• 24×7 monitoring and on-call with a 15-minute Sev-1 response commitment
• Patching, cluster and dependency upgrades on a published schedule
• Backup verification and disaster recovery drills that are actually run
• Capacity planning ahead of your seasonal peaks
• Incident management with blameless postmortems and tracked actions
• Monthly reliability, security and cost report, plus a quarterly architecture review

Coverage: follow-the-sun across EU, UK and India time zones.

Machine learning teams hit the same wall application teams hit ten years ago: the model works on a laptop and nobody can ship it safely. We apply the same delivery discipline to models, prompts and agents — versioned, evaluated, deployed and monitored.

• Reproducible training pipelines with versioned data and model artefacts
• Model registry, staged promotion and one-click rollback to a previous version
• GPU capacity management, spot strategies and queueing that controls cost
• Evaluation suites and regression gates that run before a prompt or model ships
• Inference serving with autoscaling, caching and latency SLOs
• Drift, quality and cost-per-request monitoring in production

Typical stack: Kubeflow, MLflow, Argo Workflows, Ray, KServe, vLLM, LangFuse, Weights & Biases.