An organization wants to run LLM inference on-premises, but infrastructure sizing, data boundaries and reliability are not yet established.
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ASSESS → ARCHITECT → SECURE → VALIDATE → OPERATE
Secure AI Infrastructure & Agentic Operations
Engineer private, observable and governable AI systems—where intelligent agents assist enterprise operations without bypassing established security and change controls.
Enterprise situations
Start with the operational problem, not an AI tool.
Representative challenges. Engagement scope is defined against the actual environment, security obligations and business outcomes.
AI agents need access to internal tools, yet tool permissions, identity, approval steps and failure recovery remain unclear.
Linux, Kubernetes or legacy enterprise systems could benefit from AI-assisted incident investigation without unrestricted autonomous changes.
An initial AI proof of concept works in a sandbox, but cannot yet demonstrate security, observability, audit evidence or operational ownership.
Consulting capabilities
Infrastructure engineering meets safe agent execution.
Private and hybrid AI infrastructure
Model-serving architecture, GPU/CPU placement, networking, storage, capacity, latency, cost, and on-premises integration.
Secure agent and MCP architecture
Tool contracts, permission boundaries, explicit execution policy, identity, data access, human approvals and failure handling.
Kubernetes and OpenShift AI platforms
Isolation, workload scheduling, secrets, policy-as-code, telemetry, deployment standards and operational handover.
AI-assisted infrastructure operations
Read-only diagnostics first; evidence-based recommendations and approved deterministic remediation workflows.
Enterprise integration and modernization
Connect agent workflows to supported Linux, automation, monitoring, APIs, databases and selected legacy systems.
Reliability, security and governance
Threat modeling, adversarial evaluation, rollback paths, incident response, audit records and ownership models.
Technical reference architecture
Keep reasoning, policy and execution separate.
An agent may investigate and recommend. Privileged actions should cross an explicit authorization boundary and execute through tightly scoped, auditable tools.
Enterprise users · Monitoring events · Approved applications
Agent orchestrationGoal · State · Evidence · Evaluation · Stop conditions
Private / hybrid inferenceOllama · vLLM · Evaluation
Policy and approval gatewayIdentity · Tool access · Human approval
Isolated and controlled executionKubernetes · OpenShift · KVM/QEMU · Approved Ansible runbooks
Enterprise systems · Logs and traces · Audit evidence · Verification and rollback
Conceptual design, not a claim of a completed customer deployment. Actual controls depend on the threat model and environment.
Ways to engage
Scope a tangible engineering decision.
Begin with a fixed-scope assessment or a bounded pilot. Expand to implementation once security, reliability, value and operating ownership have been demonstrated.
AI Infrastructure Readiness Assessment
Establish the baseline: requirements, constraints, technology choices, risk register, target architecture and phased implementation plan.
Typical outputArchitecture decision record + prioritized roadmap
Secure Agentic Operations Pilot
Validate one bounded use case with controlled tool access, read-only investigation, explicit approval policy, measurable evaluations and operational evidence.
Typical outputDemonstrable pilot + test results + go/no-go criteria
Enterprise AI Platform Engineering
Implement agreed model-serving and agent infrastructure, infrastructure-as-code, security controls, observability and handover practices.
Typical outputOperational platform + runbooks + acceptance evidence
Architecture and Governance Advisory
Review an existing implementation, improve its security and reliability, or guide internal teams through architectural decisions.
Typical outputDesign review + risk treatment and decision support
Engagement evidence
Leave with decisions that engineering teams can use.
Deliverables are selected during scoping; not every engagement requires every artifact.
01Current-state and target-state architecture
02Agent/tool trust-boundary and identity model
03Kubernetes / virtualization workload design
04Security and policy enforcement controls
05Evaluation datasets and failure scenarios
06Operational runbooks, observability and rollback
07Performance / capacity and cost considerations
08Implementation plan and acceptance criteria
Engineering guardrails
Operational control is a design requirement.
- Read-only investigation before privileged actions
- Least-privilege tool credentials and isolated runners
- Explicit approvals for sensitive changes
- Adversarial tests, failure injection and auditability
- Recovery planning and deterministic verification
Technology context
Enterprise LinuxKubernetesOpenShiftKVM / QEMUDockerOllamavLLMMCPAnsibleOpenTelemetryPrometheus / GrafanaPolicy-as-code
Technology is chosen to suit the system, data boundaries and operating model; this is not a requirement to adopt every product listed.
Connected expertise
Consulting, implementation and skills development—connected, not combined.
Start with the problem
Discuss your infrastructure, constraints and intended outcome.
Share your current platform, data boundaries and the first use case you want to evaluate. We can scope an architecture review or a bounded pilot without presuming a specific vendor or stack.