My background spans finance, operations, software engineering, and AI — which means I approach projects differently from most technical consultants.
Before discussing models or architecture, I focus on the business outcome: where value is created, how success will be measured, and what constraints the system must operate within.
Over the last seven years, I've worked across software, analytics, automation, and AI systems. Today my work focuses on production-grade AI architecture — combining governance, reliability, observability, and cost control into systems that organizations can trust in real-world environments.
The goal isn't to build impressive demos. The goal is to build systems that remain useful, accountable, and valuable long after deployment.
I start with workflows, governance requirements, operational constraints, and business outcomes.
The result is AI that doesn't just demonstrate capability — it survives production, remains auditable, and delivers measurable value long after deployment.