AI Infrastructure
Platforms and tooling that make AI deployable, governable, and cost-efficient in production.
What's broken
AI prototypes don't survive production constraints.
Costs are unpredictable and governance is unclear.
Security, compliance, and reliability are afterthoughts.
What we acquire / build / operate
MLOps and model lifecycle platforms
Observability, governance, and policy enforcement
Cost optimisation and inference efficiency
Secure data pipelines and feature stores
Reference solutions
Model monitoring and drift detection
Guardrails and policy engines
FinOps for AI workloads
Private deployment toolchains
KPIs we improve
Cost per inference / per outcome
Deployment frequency and lead time
Reliability and latency
Compliance readiness
Incident rate and recovery time