AI service
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.
01 / What changes for you
02 / Stack
The languages, frameworks and models we use to ship ai cloud infrastructure — model-agnostic by design, each workload routed to the right model for quality, latency and cost.
Tech stack
Models we use
03 / Infrastructure
04 / Process
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.
05 / Good questions
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.
06 / More
Start
Tell us the problem — we’ll scope a pilot and ship something real. A senior engineer, not a sales rep, writes back.
Let’s manage or open the client Kanban
