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Expertise

Tools evolve. Engineering principles endure.

Deep, hands-on knowledge of the tools below, applied with judgement. We recommend changing a tool only when the gain clearly outweighs the cost of moving.

Stack

Technologies by discipline.

CloudPublic cloud platforms we design for, migrate to and operate on.
  • AWSMulti-account organizations, networking, EKS, serverless and data services.
  • Microsoft AzureLanding zones, hub-and-spoke networking, AKS and hybrid connectivity.
  • Google CloudResource hierarchy, shared VPC, GKE and managed data platforms.
InfrastructureProvisioning, containers, orchestration and virtualization.
  • TerraformModule design, state architecture and automated plan and apply.
  • OpenTofuOpen-source Terraform-compatible provisioning and migrations to it.
  • KubernetesCluster design, multi-tenancy, autoscaling and policy.
  • DockerContainer images that are small, reproducible and secure by default.
  • VMwareOn-premises estates, hybrid connectivity and migration planning.
DeliveryBuild, test and deployment systems from commit to production.
  • GitHub ActionsReusable workflows, OIDC to cloud providers and hardened runners.
  • GitLab CI/CDPipeline templates, environments and merge request workflows.
  • JenkinsMaintaining, stabilizing and migrating existing Jenkins estates.
  • Argo CDGitOps delivery to Kubernetes with progressive rollouts.
ObservabilityMetrics, traces, logs and the dashboards built on them.
  • PrometheusMetrics collection, recording rules and SLO-based alerting.
  • GrafanaService dashboards organized around health and user journeys.
  • OpenTelemetryVendor-neutral traces, metrics and logs with shared context.
AutomationLanguages and tools for configuration and operational automation.
  • PythonOperational tooling, integrations and infrastructure utilities.
  • BashSmall, careful scripts where a shell is the right tool.
  • AnsibleIdempotent host configuration and orchestrated changes.
SecurityIdentity, secrets and security built into the delivery path.
  • IAMLeast-privilege access models, federation and workload identity.
  • Secrets ManagementCentral storage, short-lived credentials and rotation.
  • DevSecOpsScanning, policy and provenance built into pipelines.
AI infrastructureModel serving, retrieval and governed access to models, run like the rest of production.
  • LLM APIsOpenAI, Anthropic, Amazon Bedrock and Azure OpenAI behind one gateway, with evaluation and cost tracking.
  • vLLMSelf-hosted open-weight models with continuous batching on GPU nodes.
  • pgvectorRetrieval stored next to the data in PostgreSQL, under the same access rules.
  • Model Context ProtocolScoped, audited connections between AI assistants and internal systems.

Product names are trademarks of their respective owners and are used only to describe the technologies we work with.

Selection

How we choose tools.

  • Fit the team, not the trend.

    The best tool is often the one your team already operates well. We recommend changes when the gain clearly outweighs the migration cost.

  • Prefer open standards.

    OpenTelemetry, OCI images, declarative configuration and portable infrastructure code keep options open and avoid avoidable lock-in.

  • Managed where it is commodity.

    Run yourself only what differentiates you or what a managed service cannot do well. Operational load is a real cost.

  • Everything reproducible.

    Whatever we choose is versioned, declared as code and rebuildable from a clean starting point.

Contact

Let’s build better systems

Whether you’re modernizing infrastructure, improving delivery pipelines, or building a new engineering platform, let’s talk.

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