Build and manage your AI network with us.

A complete enterprise AI practice

We don't start with hardware. We start with what your team is trying to achieve, then assess your existing infrastructure, select the platforms and technologies that actually fit the workload, and design and deploy a production-grade AI environment around it. Everything below is part of that same practice, not a menu of disconnected services.

Infrastructure

• GPU compute architecture: NVIDIA H200, H100, A100, and L40; AMD MI300; AWS Trainium and Inferentia
• Cisco-based AI fabrics for high-density GPU racks
• Training clusters with high-speed interconnects (InfiniBand, RoCE)
• Inference platforms, batch and real-time, cloud or on-prem

AI Networking

GPU workloads live or die on the network between them. We design GPU fabrics for the east-west traffic patterns AI training actually produces: high-speed Ethernet and InfiniBand, RDMA and RoCEv2 for low-latency, lossless transport between GPUs, and network isolation that keeps AI traffic segmented from the rest of your environment.

AI Strategy & Consulting

Before infrastructure gets built, we work through what the workload needs to achieve, what data and compliance constraints apply, and what "production-ready" means for your team. From there we assess your existing environment, help you select the right platforms and hardware, design the target architecture, and deploy it into production.

AI Integrations

• API integration into existing business systems and workflows
• Workflow automation connecting AI outputs to operational processes
• Hybrid AI deployments spanning on-premises and cloud
• Secure deployment patterns for handling sensitive data

Fine-Tuning & RAG

We support fine-tuning strategies, retrieval-augmented generation (RAG) architectures, and enterprise knowledge integration on top of the foundation models you choose to use, along with the model deployment and inference infrastructure to run them in production. We don't build foundation models; we make the ones you've chosen work well with your data and your infrastructure.

AI Cost Optimization

• GPU sizing matched to actual training and inference workloads, not worst-case estimates
• Cloud-vs-on-prem cost modeling for AI compute
• Spot capacity, autoscaling, and right-sizing for inference
• Operational efficiency reviews once workloads reach steady state

Operations

MLOps tooling (experiment tracking, model registry, CI/CD for models), data pipelines and feature stores, and 24/7/365 managed operations and incident response are part of every engagement, not an add-on.