A major U.S. healthcare provider set out to prove that enterprise AI workloads could run securely on-premises — but integrating Cisco UCS, Red Hat OpenShift, Isovalent, Cisco AI Defense, NVIDIA GPUs and Splunk had stalled for months across siloed teams. Ngenium engineers stood the full stack up, working and observable, within days.
The work described in this use case was delivered by Ngenium engineers, and reflects the hands-on Cisco Secure AI Factory expertise the team brings to every customer engagement. The customer is described by profile only and is not identified.
During discovery, Ngenium engineers and Cisco worked with the provider’s infrastructure, security, OpenShift and operations teams to translate broad AI infrastructure goals into concrete, measurable priorities.
Each challenge reflected the same underlying reality: Cisco’s Secure AI Factory represents a fundamentally new operating model — one that demands deep, coordinated expertise across infrastructure, container networking, AI security and enterprise observability, all working in concert. Very few internal teams anywhere hold that combination in-house. The evaluation demanded a partner who could deliver the full stack, prove it working, and connect it to the operational outcomes the provider’s teams needed to see.
Rather than four separate infrastructure projects, the provider’s teams saw a cohesive platform in which security policy defined at the infrastructure layer extends automatically to AI workloads, application containers and operational tooling.
Deploying the environment was the beginning, not the end — the engagement was built to deliver a working foundation fast, then build operational value systematically.
In less than a week, the engagement delivered what internal teams had been working toward for months — a validated Secure AI Factory environment with workload-level security and observability already in place.
Cisco Secure AI Factory proven on-premises in a complex, regulated enterprise environment — months of stalled deployment turned into a working, validated platform within days, and a credible foundation for the provider’s broader AI strategy.
Isovalent Cilium and Tetragon, combined with Cisco AI Defense, delivered workload-level enforcement and process-level behavioral visibility across AI and application workloads — capabilities traditional network tooling could not provide.
Splunk across OpenShift, Intersight and infrastructure telemetry gave operations a single view of GPU utilization, model-validation events, infrastructure health and workload behavior — enabling faster troubleshooting, proactive lifecycle planning and AI-assisted incident response.
The engagement validated Cisco’s Secure AI Factory architecture in one of the most demanding enterprise environments in healthcare, expanding the provider’s footprint across UCS, Intersight, AI Defense and Isovalent — and reinforcing Cisco as its strategic AI-infrastructure standard.
We do not name customers without explicit written permission. This engagement was delivered for a major U.S. healthcare provider with an enterprise-scale server estate and on-premises AI infrastructure spanning compute, GPU, container and security layers.
Delivered. Ngenium engineers deployed and validated the Secure AI Factory foundation in the provider’s environment — this is a completed engagement, not a lab exercise or a proof of concept on synthetic data.
Cisco Secure AI Factory: Cisco UCS with NVIDIA GPUs and Cisco Intersight for compute and lifecycle; Red Hat OpenShift with Isovalent Cilium and Tetragon for container networking and process-level security; Cisco AI Defense for model-level protection; and Splunk Enterprise & Observability as the unified observability layer.
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