Most AI startups do not fail because of weak models; they fail because they cannot successfully move from prototype to production. Building a demo with a large language model or a machine learning pipeline is relatively straightforward today, but productionization introduces a completely different set of constraints including reliability, latency, cost control, and system integration. The gap between a proof of concept and a production-grade system is often underestimated, leading to architectural decisions that do not scale beyond initial experimentation. One of the most common failure points is the lack of robust data infrastructure. AI systems are fundamentally data-dependent, yet many startups rely on static, poorly curated, or insufficient datasets during early development. In production, data pipelines must handle continuous ingestion, validation, transformation, and versioning; without this, model performance degrades over time due to data drift and distribution shifts.…