Specialized AI models are built to perform specific tasks or solve particular problems. But if you’ve ever tried to fine-tune or distill a domain-specific model, you’ve probably hit a few blockers, such as: Not enough high-quality domain data, especially for proprietary or regulated use cases Unclear licensing rules around synthetic data and distillation High compute costs when a large model is excessive for targeted tasks Slow iteration cycles that make it difficult to reach production-level ROI These challenges often prevent promising AI projects from progressing beyond the experimental phase. This post walks you through how to remove all four of these blockers using a production-ready, license-safe synthetic data distillation pipeline.…