Service 07
Deployments
Cloud infrastructure, CI/CD, model operations and on-call — the discipline that turns a working build into a dependable service.
Discuss this service →
Overview
What this actually involves
We treat deployment as a product surface. Infrastructure as code, reproducible environments, progressive rollout, and rollback that has been rehearsed rather than assumed.
For AI workloads that means model registries, versioned prompts and datasets, drift monitoring, and cost controls per tenant and per feature.
Process
How we deliver it
01Process traceTwo weeks inside the work: every step, exception and handoff, timed and costed. The output is a map — automate, keep human, delete.Process map, business case, delivery plan
02Architecture & evaluation designSystem design, tool contracts and the evaluation set built from your historical cases. The release bar is agreed before code is written.Architecture note, golden set, success criteria
03Vertical slice buildOne complete workflow at a time, in production, used by real people. Weekly demo, fortnightly release.Working software in production
04Evaluation & hardeningAdversarial testing, confidence calibration, escalation tuning and load behaviour under real volume.Evaluation report, tuned thresholds
05DeploymentInfrastructure as code, progressive rollout, rehearsed rollback and cost attribution per feature and tenant.Reproducible environments, runbooks
06Operate & improveOn-call, drift monitoring and a monthly cycle that feeds production traces back into the evaluation set.Monthly operations review
Benefits
What you get out of it
Infrastructure as codeReproducible environments, reviewed like any other change.
Progressive rolloutCanary and shadow deploys with automatic rollback triggers.
Model operationsVersioned prompts, datasets and evals with drift alerts.
Cost governancePer-feature and per-tenant spend visible and capped.
Other services
