AI factory infrastructure & expertise

AI factories that run anywhere — and the experts who make them deliver.

CloudsAI is a deploy-anywhere AI platform, plus the consulting, services, and training to put it to work — on Nvidia, AMD, and Intel accelerators, from public cloud to fully air-gapped.

Talk to an architect ↗ Explore what we do
4Ways to work with us
4+Accelerator families
AnyEnvironment, incl. air-gapped
cloudsai-deploy.yaml 
# one spec — every environment
apiVersion: cloudsai.io/v1
kind: AIFactory
metadata:
  name: research-cluster
spec:
  accelerator: nvidia-h100
  environment: air-gapped
  devex:
    jupyterlab: enabled
    runtime: cuda-12.4
    kernels: preinstalled
  tunables:
    nvlink: auto
    fabric: rdma
Where it started

Teams were stuck between two bad choices.

AI was taking off and we were good at it — but everywhere we looked, running it meant picking the lesser of two frustrations.

✕ Option A — the big cloud

Powerful GPUs, painful tooling.

The hyperscalers had the accelerators. But the tooling around them was fragmented, fragile, and frustrating — so every team rebuilt the same scaffolding from scratch.

✕ Option B — on-prem

Private and cheap — in theory.

On-prem promised control and lower cost. In practice it demanded deep infra expertise, long-running dev-ops toil, and a developer experience that drifted from team to team.

The bet was never on cheap hardware — it was on a platform that erases the difference.

The real bet

A deployment contract — not a deployment script.

The flagship product is an AI platform that deploys into any environment — cloud, on-prem, hybrid, or edge — while giving developers a consistent, prebuilt, enterprise-grade experience everywhere.

At the center is something deceptively small: a deployment specification that already knows the tunables. We've characterized the major accelerators down to the fabric — and the same spec extends to whatever silicon comes next, with fully supported, tested software packages.

Nvidia H100 AMD MI200 / 250 / 300 Intel Gaudi + next accelerator

The platform contract

infrastructure ↔ development
  • A self-managed instance inside the enterprise's own VPC or data center.
  • Preinstalled JupyterLab, Python tooling, GPU drivers and CUDA — configured and tested.
  • The same CLI, notebook kernels and API clients as the hosted cloud version.
  • Zero code changes between on-prem, private cloud and public cloud.
The day the thesis changed

The question no one else in the room had asked.

The first time the leadership team pitched to a large Indian enterprise, the conversation turned on a single sentence.

"Can I run this on my own data centers, air-gapped — still using the same notebook experience our data scientists already love?" — the enterprise CIO

The product lead smiled, opened the laptop, and walked through a deployment flow. No slideware — just the platform doing exactly what the CIO had asked for.

It was a co-designed contract between the infrastructure team and the development team. The real product wasn't the hardware underneath — it was an AI platform with a stable, tested, fully supported developer experience that behaved the same way no matter where it ran.

// the walkthrough

What the laptop showed

  1. Spin up a self-managed instance inside the enterprise's own VPC or data center.

  2. Land on preinstalled JupyterLab, Python tooling, GPU drivers and CUDA — already configured.

  3. Keep the same CLI, kernels and API clients from the hosted cloud version. Zero code changes.

That was the day the thesis stopped being a pitch and became a product — and the foundation for everything else we now do.

What we do

Four ways to put our expertise to work.

The platform is the flagship — but it isn't the only way to work with us. Whether you want a product, a plan, a team, or skills, CloudsAI meets you where you are.

Platform Strategy & Consulting Enterprise AI Services AI Training
Product · self-managed platform

The CloudsAI Platform

A deploy-anywhere AI factory. One deployment contract, one developer experience — public cloud, private VPC, on-prem, or fully air-gapped.

  • Preconfigured JupyterLab, CUDA / ROCm, drivers and kernels
  • The same CLI, notebooks and APIs in every environment
  • Tuned for Nvidia, AMD and Intel accelerators
  • Runs self-managed inside your own infrastructure
Built forTeams who want a product they can deploy, run and own.
Advisory · strategy & architecture

AI Strategy & Consulting

Transformation strategy and technical architecture from people who've built cloud platforms — so you commit with a clear plan, not a guess.

  • AI transformation strategy, operating model and roadmap
  • Cloud vs. on-prem vs. hybrid infrastructure strategy
  • Accelerator selection and reference architecture
  • Total cost of ownership and capacity modeling
Built forLeaders shaping where, how and why to invest in AI.
Hands-on · optimization & operations

Enterprise AI Services

We tune and operate your AI factory with you — the full stack, from accelerators to fabric — and keep it fast, dense and reliable.

  • Accelerator characterization and performance tuning
  • Cluster scaling, capacity and topology design
  • Networking and storage architecture optimization
  • AIOps — observability, automation and SLAs
Built forEnterprises running serious GPU fleets that must perform.
Enablement · skills & adoption

AI Training

Role-based training that turns AI adoption into in-house capability — across AI, ML, GenAI and data science, on your real stack.

  • AI / ML foundations and GenAI adoption tracks
  • Data science upskilling for analysts and engineers
  • Hands-on labs on your own accelerators and platform
  • Executive and practitioner tracks, role by role
Built forOrganizations that want their own people to own AI.
Capabilities

Expertise across the full arc of building AI.

Behind the four pillars is deep, current expertise — from LLM engineering and accelerated computing, through cloud and platform infrastructure, to governance and transformation.

Applied AI
  • GenAI & LLM Engineering
  • Agentic AI & Orchestration
  • Inference & Accelerated Computing
Cloud & Platform
  • Platform Engineering & Data
  • Cloud, Hybrid & Sovereign Infra
  • MLOps, AIOps & Observability
Trust & Transformation
  • Security, Trust & Governance
  • Delivery & Release Engineering
  • Transformation & Operating Model
Explore all capabilities ↗
What powers all four

We go all the way down to the silicon.

Platform or services, we tune across the accelerator landscape — GPUs, custom AI silicon, and edge inference — down to the runtime, fabric and storage path that decide whether they perform.

GPU & training accelerators
Nvidia

H100 & the CUDA stack

NVLink topology, MIG partitioning and CUDA runtime tuned per workload — the configuration data scientists never have to see.

CUDA
AMD

Instinct MI200 / 250 / 300

The full ROCm and HIP path characterized and supported — so AMD accelerators are a first-class target, not an afterthought.

ROCm · HIP
Intel

Gaudi accelerators

Intel Gaudi brought into the same deployment spec via SYCL and oneAPI — one contract, more silicon to choose from.

SYCL · oneAPI
Server-side & edge AI accelerators
Broadcom

Custom AI silicon & fabric

Custom server-side AI accelerators and the high-bandwidth networking silicon that binds them into a cluster — where compute meets fabric.

Custom ASIC · AI networking
Qualcomm

Cloud AI 100 & edge AI

Cloud AI 100 for power-efficient server-side inference, and Snapdragon-class silicon that pushes AI inference to the edge.

Server inference · Edge AI
Full-stack depth CUDA ROCm HIP SYCL RDMA / InfiniBand networking Storage architecture Compute topology
Deploy anywhere

One platform. Four places it can live.

Public cloud

Powerful GPUs from the hyperscalers — finally with tooling that doesn't fight you.

Private cloud / VPC

A self-managed instance inside your own virtual private cloud, fully isolated.

On-prem & air-gapped

Your own data center, no outbound connectivity required — and the same notebooks.

Hybrid & edge

Burst between environments or push inference to the edge — without rebuilding anything.

Playbooks & best practices

Every engagement leaves a playbook behind.

We don't just deliver a result — we codify how it was done. Reference architectures, tuning playbooks and AIOps runbooks become your team's standard, so the capability stays after we're gone.

The knowledge layer is what turns a project into a durable capability — and it comes with every pillar.

Reference architectures

Proven blueprints for AI factories across cloud, on-prem and hybrid.

Accelerator tuning playbooks

The H100, Instinct and Gaudi tunables, captured and repeatable.

AIOps runbooks

Observability, alerting and automated response for AI workloads.

Deployment best-practice guides

Step-by-step standards your team can run without us in the room.

Operational standards & SLOs

Service levels, capacity targets and the metrics that prove them.

Capacity & cost models

Forecasting tools that keep utilization high and spend predictable.

Who's building it

Built by people who've built the cloud — and the AI that runs on it.

Our background isn't plain infrastructure. We've worked on AI at the companies that shaped enterprise computing — and on the distributed-systems and scale problems that AI now depends on.

VMware
VMware Private AI — virtualizing and isolating GPU workloads so enterprises can run AI on their own infrastructure.
Rubrik
Securing and governing the enterprise data that AI learns from — and AI-driven cyber resilience.
AWS
Hyperscale AI — managed ML and generative-AI platforms, and purpose-built training and inference silicon.

That background is the whole point. Scaling an AI factory is a distributed-systems problem as much as an accelerator one — networking, storage, compute architecture, and operating reliably at scale. From VMware Private AI, Rubrik's AI-era data security, and AWS's AI platforms and silicon, we bring AI capability — not just the plain infrastructure beneath it.

Let's talk

Tell us where your AI lives. We'll make it deliver.

Whether you need the platform, a strategy, a team to optimize what you have, or training to build the capability in-house — CloudsAI starts with a conversation.

Talk to an architect ↗ Revisit what we do