About

I'm an AI product and operations leader at SymphonyAI, and I came to it the unusual way: 25 years running retail operations first, then building the technology those stores run on. The work that compounds across the active pilot portfolio isn't any single pilot outcome — it's the internal AI infrastructure each pilot leans on: the methodology and governance that make an enterprise AI platform's outputs audit-defensible, where every figure is traceable, model behavior is bounded, and the narrative output holds to a domain-credible standard. To be clear on scope: the platform itself is implemented by engineering and agent persona designs sit with another engineer; what I author is the methodology, the governance, and the semantic layer that run through it.

Alongside the platform work, I led two analytical frameworks at a Fortune 500 US convenience-retailer pilot — a lost-sales / out-of-stock methodology that anchored the customer's commercial reviews on the retailer side, and an approach that separated recoverable store-execution losses from non-recoverable supply-chain losses for a brand-side CPG sponsor, making brand-side ROI visible for the first time. Both became reusable internal standards across CPG pilots. I also built a repeatable Solutions Engineering onboarding system, now in active use for incoming hires.

Before SymphonyAI, two years at Focal Systems across customer success and product/AI operations — reporting to the Head of Deep Learning, co-leading the launch of Ada-Order alongside Data Science, running the data labeling pipeline for computer vision model training, and shaping product roadmap as Voice of Customer across enterprise pilots. The 25 years of Wegmans retail operations leadership that preceded that — peak P&L up to $140M, direct people-management responsibility across 20+ years of the tenure with team scope ranging from 25 to 250 direct reports (peaking at 250 direct plus 350 dotted-line sustained 5+ years), 35 departments touched with Lean/CI tools, 5 new store openings, 20+ leaders mentored — is the operational discipline underneath the AI work.

In 2025 I started building with AI in earnest, outside the day job. The personal multi-agent practice is a working environment across my own projects — a governance console for decision tracking and audit, a hub-and-spoke orchestrator across project domains, a self-evaluating intelligence pipeline with closure verification, and protocol compliance enforced via hooks. A B2B CPQ Sales SaaS for industrial distribution is in production, first prospect onboarded with their 711-SKU catalog and through the demo cycle in Feb 2026; architecture sized for their planned 100-reseller rollout. I taught myself Python, TypeScript, React, SQL, and Docker by shipping these things.

I'm looking for AI product and operations leadership roles where the work is shipping complex AI products and making sure they ship credibly — product direction, methodology, governance, and the cross-functional rhythm that gets them launched. I've spent my career understanding the problem from every angle: as the operator, the customer advocate, and the builder.