Product Manager with 7+ years of experience building AI-powered platforms, agentic systems, and data products at enterprise scale. Shipped new products from concept to widespread enterprise adoption, designed sensor-to-action architectures with governed tool execution and human-in-the-loop approvals, and scaled AI features across 2,600+ enterprise accounts.
Software development background with hands-on use of LLMs, Claude Code, and automation tooling to accelerate product delivery.
Agentic automation platform unifying ~25 internal and SaaS systems into a single execution layer. Sensor-to-action loop with approvals, outcome tracking, and a governed registry of 75+ tools the assistant can call from chat.
Net-new product launched concept-to-adoption in 5 months, reaching 800 customers. Prototyped core workflows in Claude Code to accelerate shipping with a small team.
Two scikit-learn models predicting NBA shot outcomes (AUC 0.634) and player salaries from box-score stats. Rebuilt in 2026 from a 2019 BYU class project after Microsoft retired the original Azure ML Studio backend. Stack: trained locally with Random Forests, served from Next.js with Python serverless functions on Vercel.
Live demo · GitHub
Turn-based combat between Robots, Archers, and Clerics. Create fighters, pick two, and watch a step-through battle play out. Rebuilt in 2026 from a 2019 BYU CS 235 C++ class lab as a TypeScript port preserving the original damage and ability math. Stack: Next.js + Tailwind, fully client-side simulation.
Live demo · GitHub
Browse, analyze, and run a hit-goal predictor on ~4,800 GoFundMe campaigns scraped during the March 2020 COVID-19 surge. Unified rebuild of four 2020 BYU INTEX class repos (Django + React 16 + Azure ML + ASP.NET) into a single Next.js 15 app. Logistic regression re-trained locally and shipped as JSON weights for client-side prediction, replacing the Azure ML Studio endpoint Microsoft retired in 2024. Stack: Next.js + Tailwind + Recharts on Vercel, with all ~4,800 detail pages prerendered as static HTML.
Live demo · GitHub