AI infrastructure on your terms, in your jurisdiction.
Dedicated GPUs, managed inference, and engineers who will tell you which parts you actually need. Priced monthly, deployed where your data is allowed to live.
Three ways we work on AI
GPU Infrastructure
Dedicated accelerators for training, fine-tuning and inference. Yours for the term, not queued for.
Configurations →AI Inference
Managed endpoints for open, commercial or your own custom models. We operate them; you call them.
What we operate →AI Advisory
Finding the use cases worth building, and saying so when there aren't any.
What we do →Most teams start with one and add the others. You can take any of them on their own.
GPU Infrastructure
Dedicated GPU servers and clusters for training, fine-tuning and inference.
Configurations
| Profile | GPU | Per node | Interconnect | Host | Pricing |
|---|---|---|---|---|---|
| Inference | NVIDIA L40S 48 GB GDDR6 | 4 | PCIe Gen5 ×16 no NVLink | EPYC 9334 · 512 GB 7.68 TB NVMe | Request a quote |
| Training | NVIDIA H100 SXM 80 GB HBM3 | 8 | NVLink 900 GB/s 2 × 200G IB per node | 2 × EPYC 9474F · 2 TB 2 × 7.68 TB NVMe | Request a quote |
| Cluster | NVIDIA H200 SXM 141 GB HBM3e | 8 × 4–32 nodes | 400G InfiniBand NDR rail-optimised fabric | 2 × EPYC 9554 · 2 TB shared Ceph tier | Request a quote |
Why dedicated GPUs
Your training data stays where it's allowed to be.
Model training on customer data is a data-processing activity like any other. Dedicated hardware in a known jurisdiction is a much shorter conversation with your DPO than a shared endpoint in an unspecified region.
No cross-border transfer.
The data doesn't leave the region it's permitted to sit in.
Costs you can forecast.
Monthly pricing on dedicated hardware, rather than per-second billing that punishes you for training runs that overrun.
No queuing for capacity.
The GPUs are yours for the term. They're available at 2am on a Sunday because nobody else can take them.
How you take it
Bare GPU servers
You manage the stack.
Managed Kubernetes or Slurm
We run the scheduling layer, you run the jobs.
AI Inference
Managed endpoints for open models, commercial models, or models you trained yourself. We run and monitor the serving layer; you get an API.
What we operate
Open models
Llama, Mistral, Qwen and similar, deployed and kept current.
Your own models
Fine-tuned or trained from scratch, served on dedicated hardware.
Embedding and reranking endpoints
For RAG pipelines that need to stay inside your jurisdiction.
Batch and streaming
High-throughput scoring as well as interactive traffic.
Why managed inference rather than an API provider
Latency you control.
Endpoints sit near your users and your data, not in whichever region a provider chose.
Nothing is logged elsewhere.
Your prompts and completions don't leave the environment, and nothing you send becomes training data for someone else's model.
Capacity that doesn't move.
No rate limits changing under you, no deprecation notice retiring the model your product depends on.
Model choice stays yours.
Open weights mean you can take the model and the serving config elsewhere. Same argument as the rest of our infrastructure: we'd rather you stayed because leaving would be a bad idea, not because it would be hard.
Private AI
Your model, your weights, your data, your jurisdiction. Nothing leaves the environment, nothing gets logged by a third party.
For teams with a compliance requirement or a board that has asked where the data goes, this is usually the shortest path to a yes.
AI Advisory
Most AI projects fail before any infrastructure is involved — on a use case that was never going to pay for itself, or a data problem nobody scoped. We work on that part first.
What we do
Use case assessment
Which problems in your business are actually tractable with current models, what each would be worth, and what it would cost to run. Delivered as a written assessment you can take to a board.
Data readiness
What you have, what state it's in, and what has to happen before a model can use it. This is usually where the real work is.
Architecture
Build, buy, fine-tune or prompt. Where the model runs, how it's served, what it costs at your traffic.
Compliance and residency review
What your obligations mean in practice for training data, inference logs and model outputs.
Implementation
We build it, or we work alongside your team while they do.
How we're different about this
We will tell you not to build it.
A large share of AI proposals we see don't survive their own business case, and the honest answer is a smaller project, a bought product, or nothing at all. Saying so costs us the engagement, which is exactly why it's worth hearing from us rather than from someone paid to find a use case.
Advisory is deliberately not a foot in the door for infrastructure. If the conclusion is that you should run it somewhere else, or not at all, that's the conclusion.
Tell us what needs to run.
Send us the workload, the constraints, and the region it has to live in. An engineer — not a sales rep — will tell you honestly whether we're the right fit.