AI Infrastructure · Kubernetes · Platform Ops

AI infrastructure that runs in production, not just in demos.

We build, stabilize, and operate the platform foundations that enterprise AI workloads depend on. GPU-enabled Kubernetes. Multi-site resilience. Operational discipline that scales. When your AI initiatives move from experiment to executive accountability, we make it work.

Trusted by

Fortune 500 Energy
Defense & GovCloud
AI/ML at Scale
FinTech
Healthcare Systems
90%

Faster AI deployment cycles

KServe / Knative automation

$340K

Monthly GPU cost reduction

AI/ML platform at scale

60%

Kubernetes latency eliminated

Fortune 500 energy

$190K

Annual CI/CD savings

Build pipeline optimization

What we build and operate

Four capabilities ordered by strategic priority.

01

AI Infrastructure & Platforms

The Problem

"AI initiatives stalling at the infrastructure layer"

GPU Scheduling KServe/Knative Model Serving Cost Governance
02

Kubernetes & Cloud-Native Platforms

The Problem

"Kubernetes exists, but wasn't built to scale"

RKE2/OpenShift Platform Engineering GitOps Multi-Cluster
03

Reliability & Resilience

The Problem

"Production systems that can't afford downtime"

DR & Failover Observability SRE Practices HA Architecture
04

Automation & Identity

The Problem

"Manual operations creating risk at scale"

Ansible/AWX RBAC/OIDC Policy Automation Zero Trust
Engagement Models

High-impact starting points

AI Infrastructure Readiness Assessment

2-4 weeks

Best for VPs planning AI initiatives

Kubernetes Platform Stabilization

4-8 weeks

Best for VPs with fragile K8s

On-Demand Platform & SRE Operations

Ongoing

Best for teams stretched thin

Most AI initiatives fail at the infrastructure layer. The model works in the notebook. The demo impresses leadership. Then it hits production. GPU scheduling conflicts. Storage bottlenecks. No observability. No failover plan. Cost overruns that make the CFO nervous and the CTO accountable.

Meanwhile, the Kubernetes platform that was supposed to be the foundation is struggling under workloads it wasn't designed for. The internal team is capable but stretched. The vendor who set it up is gone. And leadership wants to know why the AI roadmap is six months behind.

We've seen this across Fortune 500 energy companies, defense contractors, financial services firms, and healthcare systems. The gap is always the same: the distance between AI ambition and infrastructure reality. That's where we work.

How we work

A proven methodology for stabilizing complex platforms.

01

Assess

Review architecture, incident history, cost structure, team capacity

02

Architect

Design target-state platforms, SLO strategies, adoption plans

03

Implement

Work alongside your engineers — your team gets stronger

04

Operate

Validate improvements against agreed metrics

Why companies trust us with production systems

Infrastructure before ambition

Build foundations that let AI teams move fast

Operational discipline, not demos

Runbooks, observability, incident response

Enterprise realism

Compliance, cost pressure, organizational friction

Outcomes, not tools

Reliable platforms, not GPU/cloud sales

Enhancing Automated Compliance Enforcement

Optimizing Kubernetes Clusters for Performance

Automating Cloud Infrastructure with Kubernetes and Ansible

Implementing Zero‑Trust Identity Management for a Global Healthcare Firm

Improving Real-Time Data Analytics with Kubernetes

Accelerating Model Deployment with Kubernetes

Industrial-Grade Patch Automation: How a Manufacturer Achieved 80% Faster Updates and 91% Fewer Compliance Gaps with Red Hat Ansible

Scaling E‑Commerce Infrastructure for a National Retail Chain

Accelerating Classified Software Delivery on EKS in AWS GovCloud

Migrating to GitHub Actions for CI/CD Efficiency

Transforming Patient Care with Azure Kubernetes & Zero‑Trust Security

Enhancing Automated Compliance Enforcement

San Francisco, CA

Optimizing Kubernetes Clusters for Performance

Houston, TX

Improving Real-Time Data Analytics with Kubernetes

Phoenix, AZ

Accelerating Model Deployment with Kubernetes

Palo Alto, CA

Industrial-Grade Patch Automation: How a Manufacturer Achieved 80% Faster Updates and 91% Fewer Compliance Gaps with Red Hat Ansible

Charlotte, NC

Accelerating Classified Software Delivery on EKS in AWS GovCloud

Colorado Springs, CO

Migrating to GitHub Actions for CI/CD Efficiency

Austin, TX

Technology Partners

AWS Microsoft Azure Google Cloud Red Hat Sysdig Tigera DigitalOcean Dynatrace Rafay NVIDIA Kubecost

Ready to make AI operational?

Whether you're planning GPU infrastructure, stabilizing Kubernetes, or moving AI workloads into production — we'll assess where you are and what it takes to get there.

US-based team · All US citizens · Continental United States only