The best AI teams in production aren't waiting for labelled data. They're generating it. Synthetic data has moved from a research curiosity to a core production strategy, and the teams who've figured this out are fine-tuning domain-specific models, building better evals, and closing capability gaps at a fraction of the cost and timeline of traditional annotation. Most teams haven't made this move yet. That gap is exactly the opportunity. This session breaks down how leading engineering teams are running synthetic data pipelines today: what to generate, how to filter for quality without manual review at scale, and how to use frontier model outputs to train smaller, faster, cheaper models you actually own. It also covers where pipelines break down and how to catch failures before they compound. This is one of the highest-leverage moves available to an AI engineering team right now. The question is whether you make it before your competitors do.