I design how AI and digital platforms fit together inside real organizations: the architecture, the integrations, the guardrails, and the adoption plan that makes people actually use them. The four studies below are self-directed design work — each one rooted in problems I work on every day in enterprise learning and digital platforms.
A reference architecture for an AI assistant that answers employee questions from approved internal documents — with cited sources, permission-aware retrieval, and a full audit trail for regulated environments.
A system design for teams of specialized AI agents producing work under a coordinator — with a shared message bus, defined task states, and human approval gates before anything ships.
A target-state blueprint for modernizing a corporate learning platform: LMS, experience layer, xAPI data pipeline, and analytics — with a phased migration roadmap.
A governance framework for rolling out AI in a regulated enterprise: risk-tiered controls, vendor scorecards, data classification rules, and an adoption plan built to enable — not block.
I come from learning & development and clinical systems, which means I architect from the user's chair backwards: what decision does this system support, who has to trust it, and what makes them adopt it? I prototype fast, measure honestly, and I would rather show you a working design than describe one.