AI Platform Rollout
"Day-one AI infrastructure — not a year-long platform project"
Design and deploy the AI platforms your GCC runs on — LLM infrastructure, MLOps, data platforms, and developer toolchains — engineered for scale, governance, and rapid adoption.
Six platform surfaces
LLM infrastructure
Inference platforms, model gateways, prompt management, and observability.
MLOps platform
Training pipelines, model registry, drift detection, and deployment tooling.
Data platform
Lakehouse, feature store, and governance for AI workload access.
Developer tooling
AI-augmented IDEs, code copilots, automated review.
Governance & safety
Model risk frameworks, responsible AI policies, audit trails baked in.
Cost & observability
Per-team cost visibility, SLOs, and capacity planning for AI workloads.
A phased rollout
Design
Platform architecture and governance model agreed.
Build
Core platforms stood up; first AI workload onboarded.
Scale
Additional workloads onboarded with consistent guardrails.
Operate
Steady-state operations with cost, quality, and safety metrics.
Related sub-services
Talk to us about the AI platform
Tell us about the workloads. We will return with a platform design and rollout plan.