Infrastructure that keeps the model live.
Reproducible training, canary deploys, drift monitoring, and the cost curve under control before the GPU bill lands.
Getting a model to work in a notebook is one problem. Keeping it live for 18 months, across two retrainings, three on-call engineers, and a 4× traffic spike, is a different one. We build the second.
Training pipelines that are reproducible to the byte. Deploys that ship to 5% first and roll if the metric drifts. Observability that names the failure — not just that it happened.
And the unit economics: GPU clusters, vector DBs, and inference endpoints sized to the cost the system actually has to clear, not the spec sheet.
Where infra carries the load.
AI-Powered Automation
Workflows on infra that handles retries, exceptions, and the spike on Friday afternoon.
Read the briefLLM Customisation
Fine-tuned and quantised models served on infra sized to the cost target, not the brochure.
Read the briefAI Agent Development
Agents that reason in real time on infra built for tool calls, retries, and audit trails.
Read the briefPick the work. Set the date. Ship.
Tell us the system you need, the constraint that’s blocking it, and the date you want it live. We’ll come back with a scoped plan inside one business day.