AI Infrastructure
AI data and infrastructure
AI depends on a base that runs: platform, data, search and security should be planned and delivered together.
Why assemble a stack
Enterprise AI projects often hit bottlenecks in data supply, secrets governance, stateful components and platform operations. Sorting capabilities by layer helps with product selection and with dividing operations responsibilities later.
Capability layers
Top to bottom: scenarios on top, platform at the base.
- L8
Scenarios
Where business value lands, driven by scenarios and services.
- L7
Security
Secrets control plane plus host and internal network security.
- L6
Network foundation
Enterprise DNS / DHCP / IPAM for entry points and addressing.
- L5
Data factory
One operating hub for multi-model stateful data services.
- L4
Search and observability
Search, logs and runtime insight.
- L3
Data and storage
Enterprise storage, domestic databases and real-time streams for critical business and AI data supply.
- L2
Platform automation
Infrastructure as code for repeatable delivery and governance.
- L1
Platform and OS
Hybrid cloud, domestic OS and hyperconverged / enterprise cloud base.
Typical scenarios
-
Business AI transformation
From readiness review to data and platform paths, then scenario delivery.
-
Stateful data platform
Databases, caches, messaging and search managed together on K8s / OpenShift.
-
Secrets and agent security
Unified identity and short-lived credentials instead of secrets in scripts and configs.
How it is delivered
Solution design and product rollout connect with both service lines.