The cloud AI workloads actually need
We stand up the cloud that AI workloads actually need: GPU inference, vector stores and embedding pipelines, wired with observability and cost controls so nothing runs away on you. Multi-region, data-residency aware and compliance-ready when your customers ask for it.
The tools, languages and frameworks we reach for to ship ai cloud infrastructure.
Model-agnostic by design — we route each workload to the right model for quality, latency and cost.
Self-hosted open-weights (Llama, Qwen, Mistral) on GPU
Hosted Claude & GPT via managed APIs
Optimised inference with Triton + TensorRT-LLM
Where it runs, how it scales, and how we keep it observable.
We benchmark your workload — latency, throughput, cost and compliance needs.
Infra as code: GPU inference, vector stores and pipelines, reproducible.
We tune inference (batching, quantisation, caching) to cut latency and cost.
Dashboards, SLOs and budget alarms so spend and performance stay in line.
Whichever wins for your case. Hosted APIs (Claude, GPT) for speed-to-market; self-hosted open-weights on GPU when cost-at-scale, latency or data residency demand it. Often a hybrid.
Budget alarms, per-feature cost dashboards, caching, model routing and right-sized GPUs — so spend tracks usage and never surprises you.
We build multi-region, data-residency-aware setups and can align with SOC 2 controls when your customers require it.
Tell us the problem — we'll scope a pilot and ship something real.
