This talk covers two complementary techniques for applied AI: (1) building an agentic research system that gathers and synthesizes information, and (2) training a small reasoning model — first with supervised fine-tuning to learn structure, then reinforcement learning to improve judgment quality. Together they form an end-to-end pipeline, but each technique solves a different problem.