Production-grade reference implementations
Deeper implementations focused on how AI runs in production: backend architecture, agent orchestration, durable state, RAG, governance, observability, evaluation, CI/CD, and containerized deployment.
Security Operations · In progress
Agentic SOC Operations Platform
Reference implementation for SOC incident investigation and governed response. It compares single-agent and multi-agent architectures on the same incidents.
Planned capabilities
- Single-agent vs. multi-agent architecture comparison
- RAG over security knowledge and runbooks
- Security-tool integration
- Policy controls and human approval before response actions
- Observability and verification of agent decisions
- Deployment patterns
Interactive AI demos
Working multi-agent applications across operations, governance, architecture, security, automation, and professional services.
AI Operations
AI NOC Operator
Multi-agent operational workflow that turns network alarms into correlated incidents through RAG-powered runbooks, governance approvals, ITSM integration, outcome tracking, and operational learning.
AI Governance
AI Governance Readiness Agents
Seven-agent, evidence-driven governance assessment: Shadow AI discovery, AI inventory and ownership, risk analysis, control mapping, human decision gates, readiness scoring, and a prioritized roadmap.
Enterprise Architecture
AI Architecture Advisor
Eight-agent accelerator that turns enterprise discovery inputs into reference architectures, ADRs, governance controls, a 30/60/90 roadmap, and a TCO/ROI/NPV business case.
Infrastructure Automation
AI-Governed Infrastructure Automation
Six scenarios across SD-WAN, multi-cloud networking, OpenStack, VCF, DNS/IPAM, and network compliance, with CI/CD controls, deterministic governance, human approval boundaries, and AI-assisted remediation.
Security
AI Security Advisor
Cloud and network security posture assessment that identifies gaps, prioritizes remediation by risk, and generates a structured transformation roadmap.
Professional Services
AI SOW Orchestrator
Turns customer discovery into validated, client-ready Statements of Work through multi-agent workflow orchestration.
Practitioner showcases
Interactive projects that show how I architect solutions, lead complex programs, manage partner lifecycles, and govern decisions.
AI Architecture & Delivery
Enterprise AI Architecture-to-Production
How I lead enterprise AI initiatives across a 6-phase, 22-step lifecycle, from discovery through pilot validation, controlled go-live, operations, and recertification, with decision gates and 15 worked scenarios.
Technical Program Management
Private Cloud Modernization
How I structure and govern a private-cloud transformation: integrated planning, RAID/RACI, executive governance, delivery controls, operational readiness, and program closure.
Partner Ecosystem
Partner Lifecycle Studio
How I assess partner readiness and manage lifecycle decisions across investment, enablement, scaling, recovery, competitive response, governance, and offboarding.
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About these projects: all solutions, showcases, and scenarios use illustrative, non-client data created solely to demonstrate capability. No confidential, proprietary, or customer information is included, and any figures, timelines, and outcomes are examples generated by the tools, not client results or vendor-endorsed guidance. Product names appear only for technical context.