Trajectory

Every durable technology career starts with fundamentals. Franklin's began in enterprise IT operations — desktop support, systems administration, and multi-site infrastructure management. This wasn't a stepping stone; it was the formation of a discipline. Learning how technology actually behaves in production, under pressure, across diverse environments.

From operations to engineering. Server builds, lifecycle management, migrations, patching, disaster recovery — the full infrastructure stack. This phase deepened technical capability and introduced the complexity of enterprise-scale systems: interdependencies, change risk, and the cost of failure.

Cloud didn't replace infrastructure knowledge — it extended it. AWS and Azure introduced new operational models, but the underlying discipline remained: understand the system, manage risk, document everything. Hybrid infrastructure work bridged on-premises and cloud environments, requiring fluency in both.

Working in regulated environments — particularly energy infrastructure — demanded a governance-first mindset. NERC CIP compliance, RSA Archer administration, BES Cyber Asset management. Security here isn't a feature layer; it's a foundational operating constraint. Every change is controlled, documented, and auditable.

Automation is where operational knowledge becomes leverage. Franklin's automation work produced measurable outcomes: an 80% reduction in warranty workflow processing time, consolidation of multi-facility reporting into centralized SSRS, and a Navy deployment package that cut project costs by more than 25%. These weren't scripts — they were engineered solutions to real operational problems.

Franklin founded Ampereon AI to explore governed AI systems — not AI as a product, but AI as an operational layer that must be designed with the same rigor as enterprise infrastructure. Aionist™ introduced a layered governance architecture for AI advisory systems. Cedro™ applied structured data constraints to generative AI editorial output. Both reflect a consistent principle: AI systems require governance architecture, not just model selection.

⚠ These are technology case studies and architectural concepts, not production-deployed systems.

At Duke Energy, Franklin operates at the intersection of enterprise infrastructure, security governance, and emerging AI operations. The current focus is designing AI-assisted operations architecture — connecting Dynatrace observability signals with ServiceNow workflows through a governed, auditable AI layer. This is the synthesis of the entire trajectory: infrastructure depth, security discipline, automation experience, and AI governance thinking applied to one of the most regulated infrastructure environments in the country.

⚠ AI operations work is an architectural exploration, not a production deployment.