Expertise in Action
What we've studied, prepared and are ready to execute. Real capabilities for real infrastructure challenges.
0s
target downtime on production migrations
VMware workload migration with minimum effort and maximum control
Broadcom's licensing changes are forcing a rethink of virtualization strategy. But migrating production workloads off VMware carries real risks: hidden dependencies, incompatible formats, and maintenance windows that don't exist for systems running 24/7.
Our approach
We run a detailed assessment of each workload: dependencies, I/O, networking, and storage. We classify each VM as lift-and-shift, re-platform, or retire. We use tools like MTV (Migration Toolkit for Virtualization) and virt-v2v to automate conversion, with active-active replication patterns that allow migration without service interruptions.
What we deliver
A wave-based migration plan with tested rollback, automated post-migration validation, and flexible destination: OpenShift Virtualization, OpenStack, or public cloud depending on each workload's profile. The goal is for the operations team to not notice the difference — except on the invoice.
Our approach
We design hybrid architectures where VMs and containers coexist on the same platform. OpenShift Virtualization enables running virtual machines as native Kubernetes workloads, with the same network policies, storage, and observability. For environments requiring full hardware control, we integrate OpenStack as the IaaS layer.
What we deliver
A unified platform with centralized lifecycle management, GitOps for infrastructure and applications, integrated networking between VMs and pods, and a gradual migration strategy that allows moving workloads from VM to container when the team is ready — no pressure or artificial deadlines.
1
unified platform for VMs and containers
Hybrid environments for clusters and virtualized solutions
Many organizations operate two separate worlds: traditional virtualization for legacy applications and Kubernetes for new ones. Maintaining both doubles licensing, training, and operations costs, creating silos that hinder end-to-end visibility.
24/7
production operation with continuous day-2 support
OpenShift and OpenStack infrastructure deployment and support
Deploying a cluster is day 1. The real problem is day 2: upgrades that break operators, deprecated APIs, capacity planning without data, and troubleshooting at 2 AM without visibility. Most enterprise implementations fail at continuous operations, not initial deployment.
Our approach
We deploy OpenShift platforms (bare-metal, virtualized, cloud-hosted) and OpenStack with proven architectures for telco and enterprise environments. But the real value is in day-2 operations: tested upgrade processes with staging that mirrors production, automated smoke tests, mandatory ResourceQuotas, correlated observability (metrics + logs + traces), and GitOps with ArgoCD for full change control.
What we deliver
Production platforms with continuous operation: regular upgrades, security patching, data-driven capacity management, and a team that knows the platform inside out. We don't install a cluster and walk away — we stay and operate it.
Our approach
We enable open-source LLM models (LLaMA, Mistral, Qwen) on fully isolated infrastructure. We evaluate available hardware, select the optimal model for the use case, apply quantization to maximize performance on existing hardware, and deploy with optimized serving on OpenShift or bare-metal. All without a single external connection.
What we deliver
A functional LLM inference service within the security perimeter, with an OpenAI-compatible API, operations documentation, and training for the internal team. Includes latency and throughput benchmarks on the client's actual hardware so they know exactly what to expect.
100%
of data stays within the perimeter
On-premises LLM deployment for airgap environments
Organizations with sensitive data — financial, government, defense, healthcare — need AI capabilities but can't send information to cloud APIs. Commercial models require connectivity, and open-source models require expertise in GPU sizing, quantization, and serving that few internal teams have.
<15
minutes target MTTR for critical incidents
Production troubleshooting embedded with internal teams
When something fails in production at 2 AM, the internal team spends more time finding who to escalate to than solving the problem. Platform vendors have response SLAs, not resolution SLAs. And on-call engineers don't always have the deep context of every platform component.
Our approach
We integrate directly with internal operations teams as an extension of the team — not as an external helpdesk. We know the platform because we deployed it or we operate it. When there's a production issue, we participate in troubleshooting with real context: we know what changed, what depends on what, and how to coordinate with vendors so resolution is effective, not just an escalated ticket.
What we deliver
L2/L3 support with engineers who know the platform, direct vendor coordination (Red Hat, cloud providers) to accelerate resolutions, codified and tested runbooks for known incidents, and a continuous improvement cycle where every incident becomes prevention — not just a post-mortem nobody reads.
Our approach
We implement dashboards that compare requested usage against actual usage by team, project, and environment. Every resource is tagged with owner, cost-center, and purpose. We generate periodic reports showing exactly where oversizing exists, which resources have no owner, and how much could be saved with rightsizing based on real data — not intuition.
What we deliver
Full visibility into actual vs. provisioned consumption with Kubecost, Prometheus, and Grafana dashboards. Executive reports to present to leadership with concrete savings recommendations. Allocation governance with per-team budgets and automatic alerts. And a capacity planning process that enables sizing new requirements before provisioning — to bring order to what platform vendors request.
30%
of resources typically oversized or orphaned
Requested vs actual usage dashboards for cost optimization
Platform teams request resources based on optimistic estimates, and nobody checks if they actually use them. The result: instances at 5% CPU utilization, orphaned volumes nobody releases, and a cloud bill that grows without anyone being able to explain why or for whom.
Have an infrastructure challenge?
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