I design decision systems that make ambiguous signals legible, governable, and actionable under pressure.
Selected case studies across AI, enterprise systems, automation, and regulated environments, focused on problem framing, decision clarity, constraints, and outcomes.
Designing decision clarity in distributed data systems used by enterprise data teams.
Designing decision control in AI automation systems managing enterprise workflows.
Designing decision governance in regulated AI systems within financial services.
Designing early-stage enterprise systems from exploration to scale.
Aligning fragmented legacy systems, stakeholder ownership, and cloud licensing workflows without formal authority.
Early lessons from Friendster, Autodesk, and Evernote on how systems can appear to function while failing beneath the surface.
Advising on AI, decision systems, and governance across enterprise and civic domains, including financial institutions and national security policy contexts.
This work extends into writing and analysis, applying system-level thinking from software to civic infrastructure, public trust, and institutional decision-making.
Designing decision clarity in ambiguous environments by surfacing boundaries, aligning stakeholders, and making complex systems actionable.
Designing a system layer that makes probabilistic AI outputs usable in real decisions by exposing confidence, surfacing uncertainty, and defining when human judgment must intervene.
Applies the HARMONIC framework to bridge model behavior and operator judgment in high-stakes environments, with a focus on escalation logic, trust calibration, and decision accountability.
Designing access and observability in civic decision systems at county scale.