Cloud AI Modernisation Guide

Most enterprises already run workloads on AWS, Azure, GCP, or Oracle—but the stacks were never designed for generative AI. Modernisation is less about trendy tools and more about disciplined plumbing.

1. Establish the Control Plane

Centralise secrets, feature stores, model registries, and policy enforcement before spinning up new workloads. Without a control plane you accumulate shadow systems in weeks.

2. Separate RAG from Core Apps

We run retrieval, ranking, and generation as dedicated services with explicit SLAs. Embeddings live in managed vector stores; LLM routing goes through service meshes so we can swap providers without rewiring applications.

3. Instrument Everything

Latency, hallucination rate, citation coverage, and cost per thousand tokens become first-class metrics. Observability stacks (OpenTelemetry, Grafana, Datadog) feed dashboards that exec teams actually review.

4. Automate Releases

Kubernetes/KServe or SageMaker/Kubeflow manage deployment gates. Models hit staging with synthetic evals, then go live only after human sign-off. Canary routing keeps exposure controlled while feedback collects quickly.

pipeline {
  data = bronze -> silver -> featureStore
  models = registry.track(version, lineage)
  deploy = kserve.canary(traffic=20%)
  eval = nemo.evaluator(metrics=["factual", "tone", "guardrail"])
}

Modernising isn't an infrastructure vanity project. It's the only way to make AI launches boring—in the best possible way.