The Project:
I conceptualized and built Creemee Check-In, a mobile-responsive web application designed to track Vermont’s iconic soft-serve scene. This was a one-person project where I acted as the sole product architect, systems engineer, and UI/UX designer.

The Workflow: Orchestration over Syntax
While I hold a B.S. in Computer Information Systems, my goal for this project was to test the limits of AI-augmented rapid prototyping. I built this entire application by leveraging AI as a force multiplier. My role was Strategic Orchestrator—I didn't write the code; I engineered the solution.

Initial Conception: I started by brainstorming the vision with Gemini, asking, "What do I need to do to get started?" It acted as my technical consultant, walking me through the essential steps and architectural requirements for a web-based app.

The Build Loop: Once we established the foundation, I used Gemini to generate the specific, complex prompts required by Google AI Studio to output the app’s code.

Iterative Debugging: As I tested the app, I encountered roadblocks. I treated Gemini as my engineering team: I would feed it the specific errors I was seeing, and it would explain the "why" behind the bug and provide updated, refined prompts for me to run back through AI Studio until the feature worked exactly as intended.

Architectural Vision: I managed the codebase by treating the AI as an extended partner, ensuring that as we added features, the logic remained stable and the app remained performant.

Current State & Roadmap:
v1.0 is currently live with data persisting locally via the browser’s localStorage. My roadmap is to transition this from a personal tool into a crowd-sourced, community-driven database of Vermont’s best creemee spots.

Why This Matters for L&D:
This project is a proof of concept for AI-augmented capability. It proves that a leader with strong strategic vision can bypass traditional technical barriers. In a corporate L&D environment, this ability to identify a problem, design a solution, and orchestrate AI tools to build that solution—without waiting for technical support—is the key to scaling learning programs and rapid program deployment.