Production
AI Inference Infrastructure
We engineer the compute, model serving and inference layer that agentic workloads need — efficiently, privately where required, and at scale.
- 01REQUESTAn agent needs a model.
- 02GATEWAYThe request enters one controlled gateway.
- 03MODEL.ROUTEIt is routed to the right model.
- 04SERVEThe model is served on the right hardware.
- 05CACHERepeated work is cached.
- 06RESPONSELatency and cost are recorded.
What it is
AI infrastructure is an enabling layer for agentic systems. It is not a standalone cloud infrastructure business, and we do not sell it as one.
We engineer the compute, model serving and inference layer that the agentic workload needs, and we choose models and hardware according to the workload rather than forcing a particular provider.
When you need it
- SIGNAL 01Inference cost is growing faster than usage.
- SIGNAL 02Models must be self-hosted for data, latency or regulatory reasons.
- SIGNAL 03Latency under load is too high for the agent's workflow.
- SIGNAL 04One provider outage stops the whole system.
What we build
The engineering.
- 01
LLM inference and model serving
Serving for open, self-hosted and private models, sized for the workload.
- 02
GPU and CPU inference
The right compute for each model and task.
- 03
Model routing and inference gateways
Requests routed to the right model, with fallbacks when one fails.
- 04
Quantisation, batching and caching
Throughput and cost improved without breaking the task.
- 05
Distributed inference and GPU scheduling
Serving that scales across hardware as load grows.
- 06
Inference cost optimisation
Cost per task measured and brought down.
How it works
One run, end to end.
- 01
REQUEST
An agent needs a model.
- 02
GATEWAY
The request enters one controlled gateway.
- 03
MODEL.ROUTE
It is routed to the right model.
- 04
SERVE
The model is served on the right hardware.
- 05
CACHE
Repeated work is cached.
- 06
RESPONSE
Latency and cost are recorded.
What it integrates with
Chosen for the workload and your environment — not a preferred provider.
- Open-source models
- Self-hosted models
- Closed-source model APIs
- Model gateways
- GPU runtimes
- Custom runtimes
In production
Production is part of development.
Evaluation, security, deployment and operations begin before release — on this service as on every other.
EVAL
How we test it
- Model comparison on your tasks before any routing change.
- Load testing for latency and throughput.
POLICY
How we secure it
- Private model serving where data cannot leave your environment.
- Access control at the inference gateway.
RUNTIME
How we deploy it
- Inference in cloud, private, on-premises or air-gapped environments.
- Model versions deployed and rolled back like any other release.
TRACE
How we operate it
- AI workload observability: latency, throughput, errors and utilisation.
- Inference cost tracked per model and per task.
What you receive
Engineering outputs, not a deck.
We do not hand over a prototype and leave production engineering to you.
- 01Model serving sized and tuned for your agentic workload
- 02An inference gateway with routing and fallbacks
- 03Performance and cost baseline, before and after
- 04Private or self-hosted serving where required
- 05Workload observability for inference
- 06Capacity and cost plan as usage grows