AI Infrastructure
From the workload to the silicon.
HPC, GPU deployment, agentic systems and the hardware underneath.
Most partners sell a slice: a model shop that never touches metal, a reseller that ships a box and leaves, a cloud that rents you capacity at a multiple of owning it. We design the cluster, build it, run it, and source the hardware it runs on.
The stack
Five things, one team, one accountability.
Split these across three vendors and the gaps between them become your problem. The fabric nobody specified, the driver nobody owns, the hardware that arrives eleven weeks after the project needed it.
- HPC
- Cluster design and the parts that decide whether it scales: Slurm or Kubernetes scheduling, InfiniBand and NVLink fabric, parallel filesystems, and MPI workloads that actually use the interconnect you paid for.
- GPU deployment
- Racking, power and thermal envelope, the driver and CUDA stack, MIG partitioning, and multi-node training fabric. The unglamorous layer where most GPU projects quietly lose their throughput.
- AI infrastructure configuration
- Model serving on vLLM or Triton, orchestration, observability, tenancy and security, and the cost governance that keeps a GPU estate from becoming the line item nobody can explain.
- Agentic deployments
- Agents put into production with an evaluation harness, guardrails, human-in-the-loop review and a rollback path, rather than a demo that works once on a clean input.
- Hardware sourcing
- GPU servers, datacentre accelerators, RTX workstation cards and the networking between them, sourced through our distributor relationships and quoted against your workload.
Agentic deployments
The demo is the easy part.
An agent that works on a clean input in a meeting is not a system. These are the use cases we put into production, and underneath them, the four controls that decide whether they survive contact with real work.
Claims and document processing
Intake, extraction and routing across unstructured documents, with a confidence threshold that escalates rather than guesses.
KYC and onboarding review
First-pass review against policy, with every decision traced to the rule and the evidence that produced it.
Support triage
Classification, enrichment and routing ahead of a human, cutting the queue rather than replacing the agent.
Procurement and vendor comparison
Specification parsing, quote normalisation and comparison across suppliers.
Engineering agents
Code review, migration and test generation inside your own repositories and your own access boundaries.
What keeps them running
- Evaluation harness
- A scored test set the agent has to pass before it touches production, and again after every prompt or model change.
- Guardrails
- Hard limits on what the agent may call, spend, write to and see, enforced outside the model.
- Human in the loop
- A review step on the decisions that carry cost or risk, with the agent's reasoning attached.
- Rollback
- A way back to the previous behaviour that does not depend on the agent agreeing to it.
The honest limitation: an agent is only as good as the evaluation set you can write for it. Where a task has no checkable right answer, we will tell you so and scope it as assistance rather than automation.
Hardware
We source the metal too.
GPU servers, datacentre accelerators, RTX workstation cards and the fabric between them, sourced through our distributor relationships and quoted against the workload you describe.
The honest limitation: we are not a warehouse. We hold no stock and publish no price list, because both would be out of date before you read them. What we quote is a configuration and a lead time, and the lead time is the number that decides your project.
- Multi-GPU training servers
- Eight-way and four-way GPU chassis, with the power, cooling and NVLink topology specified against the model you intend to train.
- Datacentre accelerators
- Current-generation datacentre GPUs for training and inference, sourced to the configuration your cluster design calls for.
- RTX workstation cards
- Professional workstation GPUs for local development, visualisation and single-seat inference.
- Networking and fabric
- InfiniBand and high-speed Ethernet, switches, cabling and the optics: the half of a cluster budget most quotes forget.
How a quote works
1
Tell us the workload
Model size, throughput target, training or inference, and where it will sit. Not a part number, the problem.
2
We spec and quote
A configuration matched to the workload, with a real lead time attached to it.
3
We deliver
Hardware to your site or datacentre. We rack, configure and operate it if you want that too.
Proof
Already running, already measured.
GPU infrastructure we deployed and operate across 3 regions, on NVIDIA Blackwell B200.
10x
faster model training after the cluster was rebuilt
Training runs that took 4 weeks finish in 1 week.
99.99% availability, across 3 regions
- 3-4x
- training speed-up on B200 over the previous generation
- 8-10x
- multi-node speed-up once InfiniBand replaces Ethernet
- 50%
- lower inference cost per token after right-sizing
- 8-16 weeks
- from engagement to a first use case in production
Questions
Answers, on the record.
Do you sell GPUs, or only deploy them?+
Both, and they are the same conversation. We specify the cluster, source the hardware, install it and operate it. Sourcing is brokerage: we quote against a workload rather than hold stock, so a quote names a configuration and a lead time.
Can you source GPU hardware without the deployment work?+
Yes. Send a part number or a rough configuration and you get a specification and a lead time back. Nothing obliges you to buy the deployment alongside it.
What does AI infrastructure configuration actually cover?+
Sizing the cluster against the workload, the network and storage fabric that keeps the GPUs fed, the scheduler, drivers and container runtime, and the monitoring that tells you whether the silicon you paid for is busy.
How do agentic deployments differ from running a model?+
An agent takes actions, so it needs the controls an action needs: the boundary of what it may touch, an audit trail of what it did, a human checkpoint where the cost of being wrong is high, and a way to stop it.
Where do you deliver and operate?+
From studios in Dubai, London, Islamabad, Singapore, serving clients across 30+ countries. We respond within 24 hours on business days.
Your workload
Tell us what you are trying to run
We respond within 24 hours on business days.
Quoting hardware
A quote moves faster on WhatsApp
Send the workload, the rough configuration, or a part number you have been given elsewhere. You get a specification and a lead time back, not a brochure.
One team specifies the cluster, sources the hardware, racks it and keeps it running. Nobody in the middle to point at.