
Parkcast Lab
End-to-end ML concept turning granular neighborhood data into probabilistic parking forecasts, shown as color-coded street segments over a static city map.
Interactive demos showcasing production-grade AI applications I've built, from concept to implementation. Each demo highlights both technical execution and product thinking.
Demos use sample data with rate limits and input validation to manage API costs.

End-to-end ML concept turning granular neighborhood data into probabilistic parking forecasts, shown as color-coded street segments over a static city map.

Measuring market confidence across portfolios through the transformation from raw reports to governed AI insight.

AI-powered interview analysis for product discovery that bakes in company-ready methods, structure, and compliance while enforcing strong security, embedded guardrails, and full traceability.

A visual, interactive tool that shows AI cost optimization opportunities in real-time, with before/after comparisons and actionable recommendations.
Each project is aiming to demonstrate end-to-end thinking: from identifying user needs and defining success metrics, to technical architecture and polished UX. These aren't just code samples, they're products designed with real-world constraints in mind, including cost optimization, security, and scalability.