From AI ambition to production infrastructure.
We design and deliver the compute, networking, data, and governance layers that make enterprise AI fast, secure, and economical, whether you run it on-premises, in the cloud, or both.
Everything between the model and the metal.
GPU Cluster Architecture
Training and inference clusters built on NVIDIA HGX / DGX-class systems and other accelerated platforms, sized to your models and workloads.
- Node, rack, and pod-level design
- InfiniBand and Spectrum-X / RoCE Ethernet fabrics
- High-performance parallel storage tiers
- Power and cooling envelopes for 40–130+ kW racks
Private & Hybrid AI Platforms
Keep sensitive data in your control with on-premises or sovereign AI, and burst to cloud when it makes sense.
- Kubernetes and GPU operator stacks
- Model serving and inference optimization
- Multi-tenant scheduling and quotas
- Cloud GPU cost and capacity strategy
LLM, RAG & Agentic Solutions
Reference architectures for retrieval-augmented generation, copilots, and governed AI agents that automate real work.
- RAG pipelines and vector search
- OpenAI, Anthropic, and open-model integration
- Agent orchestration with human-in-the-loop approvals
- Evaluation, testing, and observability
AI Security & Governance
Controls that let you adopt AI without leaking data or creating unmanaged risk.
- Secrets redaction and data-loss prevention
- Identity, access, and audit trails for AI systems
- AI-augmented threat modeling
- Policy aligned with NIST AI RMF practices
AI Readiness Assessment
A clear-eyed view of your data, infrastructure, skills, and use cases, with a prioritized roadmap and budget.
- Use-case discovery and ROI scoring
- Infrastructure and facility gap analysis
- Build vs. buy vs. cloud decision model
- 12–24 month execution roadmap
MLOps & AI Operations
Operational foundations so models move from notebook to production, and stay healthy once there.
- CI/CD for models and data
- GPU utilization and cost telemetry
- Automation with Python, Terraform, and Ansible
- Runbooks and team enablement
We build AI systems, not just slide decks.
Our principal architect builds agentic AI platforms hands-on today: orchestrated multi-agent engineering pipelines, governed-autonomy approval models, and credential-isolation pipelines that keep secrets out of prompts, logs, and model context. That same discipline goes into every client design.
Frequently asked questions
What does an AI infrastructure architect do?
An AI infrastructure architect designs the compute, networking, storage, power and governance layers that AI workloads run on. That includes sizing GPU clusters, choosing InfiniBand or Ethernet fabrics, planning storage throughput, and making sure the facility can power and cool the hardware.
Should we run AI on-premises or in the cloud?
It depends on data sensitivity, utilization and budget. Steady, high-utilization training and inference workloads are often cheaper on-premises or in colocation, while bursty or experimental work fits the cloud. Most enterprises land on a hybrid model, and we model the costs of each option before you commit.
Can you design and source an NVIDIA GPU cluster?
Yes. We design NVIDIA HGX and DGX-class clusters, the InfiniBand or Spectrum-X networking that connects them, and the storage and power behind them, then source the hardware through authorized distribution with an architect-validated bill of materials.
How do you keep enterprise AI secure?
We isolate credentials and secrets from prompts, logs and model context, apply identity and access controls to AI systems, keep audit trails, and align governance with frameworks such as the NIST AI Risk Management Framework.
What is an AI readiness assessment?
A structured review of your use cases, data, infrastructure, facilities and skills that ends with a prioritized roadmap, a build-versus-buy-versus-cloud recommendation and a realistic budget.
Let’s design your AI platform.
Tell us about your models, data, and timeline. We will come back with an architecture, a bill of materials, and a realistic budget.