The AI StackCompany

The AI Stack Company

From machine
to model.

Owning an AI stack is not one problem. It is twenty-one, stacked. We build them, operate them, and run them for you.

Hire us → See the stack
L0 Site & Facility L1 Machine L2 Network Fabric L3 Storage & Data L4 Provisioning L5 Node Runtime L6 Validation L7 Scheduling L8 Observability L9 Platform & MLOps L10 Training L11 Inference & Serving L12 Models & Evaluation L13 Applications & Agents
Fourteen layers. Each supplies capabilities the one above assumes.

The layer model

We know the layers.

L13 Applications & Agents What does the business actually consume? Without it Nothing technical — but the stack becomes a cost centre defending its own existence.
L12 Models & Evaluation Which model, and is it good enough? Without it Leaderboard-driven selection, and licence conditions discovered after the product ships.
L11 Inference & Serving Can you serve tokens at a viable price? Without it Unit economics. A default configuration gives up ~29% of achievable throughput.
L10 Training Can you train at scale without losing weeks? Without it Runs land at 35–45% MFU against a 40–60% achievable range. The fix is configuration, not capital.
L9 Platform & MLOps How do workloads get packaged and shipped? Without it Reproducibility and team throughput. The GPUs are busy but the science stops compounding.
L8 Observability What is happening, and what does it cost? Without it Attribution. A slow job could be GPU, fabric, storage, scheduler or code. Guessing is expensive.
L7 Scheduling Who gets the GPUs, when, in what topology? Without it Utilisation. A debt-financed cluster breaks even near 70%.
L6 Validation Is every node still good? Without it Grey nodes — degraded but not failed. They pass every dashboard and drag the whole cluster.
L5 Node Runtime Is the environment actually correct? Without it Driver, CUDA, NCCL and firmware must be coherent. Nothing owns that coherence by default.
L4 Provisioning Does bare tin become a known-good host? Without it Configuration drift. Nodes diverge and a training job hangs on the slowest inconsistency.
L3 Storage & Data Does data reach the GPU fast enough? Without it Storage I/O costs 15–30% of GPU utilisation in typical multi-node clusters.
L2 Network Fabric How do accelerators talk to each other? Without it Four separate networks, not one. A single mis-tuned setting costs 30–50% of collective bandwidth.
L1 Machine Which silicon, in what shape? Without it The least reversible decision in the stack. Wrong shape locks in the cost base for years.
L0 Site & Facility Can the building physically host it? Without it An NVL72 rack draws ~130 kW and weighs 1.36 t. Most enterprise halls are built for 5–15 kW.

Hover a layer to see what breaks without it

And seven that cut across

Not a final phase.

L0 Site & Facility L1 Machine L2 Network Fabric L3 Storage & Data L4 Provisioning L5 Node Runtime L6 Validation L7 Scheduling L8 Observability L9 Platform & MLOps L10 Training L11 Inference & Serving L12 Models & Evaluation L13 Applications & Agents X1 Security X2 FinOps X3 Energy X4 People X5 Procurement X6 Reference X7 Service
The seven cross-cutting concerns are not a final phase. They pass through every layer.

Why this is hard

You didn’t buy GPUs.
You bought a staffing problem.

Every layer is a different profession, hired from a different labour market. AI skills are now the hardest in the world to recruit for — ahead of all engineering and IT.

What you hire us for

Assess. Design. Build. Operate. Enable.

  1. 01 Assess Where you actually are. Days
  2. 02 Design An architecture that survives contact with reality. Weeks
  3. 03 Build Provisioned, configured, validated, evidenced. Weeks–months
  4. 04 Operate The layers you cannot staff. Ongoing
  5. 05 Enable So you need us less. Ongoing

You hire us.
We get this done.

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