The platform engineering that keeps AI running after launch week
Reproducible pipelines, a registry with real lineage, GPU scheduling, tested rollback and quality monitoring — handed over with runbooks.
Operations
The first deployment is a project; everything after it is an operation, and that is where the cost and the risk live. Models drift, prompts change with less review than code, GPU spend loses its owner, and the first incident finds no runbook.
Rollback
<5 min
target time to revert a bad model or prompt release
Utilisation
60–80%
sustained GPU utilisation a well-scheduled shared cluster holds
Delivery
Prompts, eval sets, retrieval configs and model versions pass the same gates as the application. Infrastructure is declared, so a cluster rebuilds from the repository, not memory.
Scheduling
Quotas, priority classes, pre-emption and gang scheduling on Kubernetes or Slurm, with queue times published per team and idle allocations reclaimed automatically.
Monitoring
We trace requests through retrieval, model and tool calls, score sampled live traffic against the CI eval set, and watch the inputs for drift, because they move first.
Orchestration
Delivery
Tracking
Observability
Governance
Who we do this for
2–3 weeks
An audit of how your models get built, deployed, monitored and paid for, with the gaps ranked by risk.
Readiness audit, ranked risk register, prioritised remediation plan
8–16 weeks
Pipelines, registry, GPU scheduling, deployment gates, monitoring and cost attribution on the estate you already have.
Declarative repositories, populated model registry, dashboards, runbooks
Ongoing
We hold the pager under an SLA, covering incident response and model and platform upgrades.
On-call rota, incident reports, quarterly capacity and cost review
Less than people expect. We build on your pipelines and observability rather than replacing them. What is new: the artefact is weights rather than a binary, the tests are statistical, the hardware needs scheduling rather than autoscaling, and quality breaks from prompt or data changes that never touched the code.
No, and building it early is an expensive mistake. With one model, one application and one team, a registry, a deployment pipeline and a quality dashboard are enough. Quota management, multi-tenant governance and cost attribution earn their keep once several teams share GPUs. We will scope the smaller thing.
Slurm for large batch training on a fixed cluster, where gang scheduling and fair-share queues are the daily reality and researchers know the tooling. Kubernetes for inference, mixed workloads and wherever the rest of your estate lives. Plenty of organisations run both, joined by a shared registry and object store.
Send us the constraint you are actually up against — budget, latency, regulator, deadline.
