Kubernetes infrastructure designed for AI workloads, production operations, and teams that need it to hold up.

We architect and design Kubernetes platforms for organizations moving AI and ML workloads from pilots into production. The focus is on building a foundation that supports demanding compute, scales predictably, and gives your team a clean operating model — not just a cluster that runs today.

By the numbers

<2mo Time to production cluster

60% Less Day 2 overhead

30% Lower provisioning cost

100% GitOps-driven lifecycle

Who this is for

Startups running AI inference or training workloads that need to scale without re-architecting
Financial institutions that need bare metal Kubernetes with CAPI and strong compliance controls
Teams moving from legacy setups or early proofs of concept to production-grade platforms
Organizations that need multi-cluster, hybrid, or air-gapped Kubernetes operating models

What we cover

Core capabilities.

Cluster provisioning and lifecycle automation

Declarative cluster management with Cluster API, GitOps-driven lifecycle using Flux or ArgoCD, and version-controlled infrastructure that supports repeatable provisioning, automated upgrades, and self-healing behavior.

Architecture design for your workload

Cluster topology, networking (Calico, Cilium, or Multus), storage orchestration, and multi-cluster patterns shaped by your workload profile, compliance requirements, and operational constraints — not a generic template.

GitOps and infrastructure-as-code

Infrastructure-as-code with Terraform, Crossplane, or Pulumi paired with GitOps delivery so every change is auditable, rollbacks are safe, and Day 2 operations don't require manual intervention.

Self-healing and operational automation

Recovery-friendly infrastructure using Ansible for configuration and remediation, combined with Kubernetes-native health management, so failed components can be rebuilt from known-good state without manual intervention.

Security and compliance

RBAC, network policies, runtime security, audit logging, and image scanning designed in from the start — with alignment to HIPAA, FINRA, GDPR, SOC 2, or FedRAMP requirements where needed.

GPU workload support and cost efficiency

GPU-aware scheduling, right-sizing, autoscaling, and cost-aware capacity decisions so AI inference and training workloads can grow without re-architecting the platform later.

Next Step

Ready to build a Kubernetes platform that holds up?

Book a 30-minute discovery call. We'll look at your current infrastructure and tell you exactly what needs to change.

View case studies