Beyond Frontier Models: Building Closed-Loop LLM Systems That Learn from Outcomes

Frontier models are powerful generalists, but the future of LLM applications will be won by systems that are specialized, measurable and continuously improving. Xinchi Qi, co-founder and CTO of Revamp AI, shares how Revamp uses open-source models, custom training, tight feedback loops, online evaluation and runtime guardrails to outperform frontier models on domain-specific production tasks. He covers how live business outcomes become training signals, how smaller models can become faster, cheaper, and more reliable than general-purpose APIs, and why the next generation of LLM applications will be closed-loop, governed, and outcome-driven from day one.