14 Days to Production: A Quantitative Case Study in AI-Assisted Development

This paper presents a quantitative analysis of AI-assisted software development through the lens of StitchLab, a production-grade SaaS platform developed over 14 calendar days. Through systematic analysis of 205 commits, I examine AI contribution rates (64.9% of commits), fix-to-feature ratios (1.39:1), and error patterns requiring human intervention. The project produced 122 API endpoints across 16 domains, supported by 1,351 meaningful test cases developed using test-driven methodology. Compared to traditional approaches, I estimate 3-4x acceleration, with primary time savings in boilerplate generation and test scaffolding. My findings reveal that while AI significantly accelerates development, it exhibits systematic limitations in integration testing and CI/CD configuration. I propose "disciplined AI-enabled engineering"—characterized by upfront architectural documentation—as distinct from the emergent "vibe coding" paradigm. These findings have implications for team composition, development workflows, and human oversight allocation in AI-augmented projects.