A recurring technical strategy for optimizing specialized workflows involves fine-tuning open-source models using proprietary datasets followed by a reinforcement learning (RL) stage. This approach has demonstrated superior performance over frontier models in several high-stakes domains. At Bridgewater Associates, expert-labeled data was used to train an open-source model to identify document relevance for investment theses; this model achieved a 30% reduction in error rates compared to leading frontier models while significantly lowering inference costs. In the legal sector, Harvey applied RL to an open-weight model to handle long-horizon tasks like transaction due diligence and memo drafting. Their specialized agent outperformed both GPT-5.5 and Claude Opus 4.8 according to internal rubrics. Finally, Intercom developed "Fin Apex," a vertical support model post-trained on billions of customer service interactions to optimize unit economics. By moving away from expensive frontier model per-call pricing, Fin Apex manages nearly two million weekly resolutions with higher success rates and lower operational costs than its larger counterparts. These cases illustrate a shift toward using RL and domain-specific post-training to surpass general-purpose models in accuracy and cost-efficiency.