We stopped optimizing our LLM stack manually — it optimizes itself now

Three months ago we were manually picking which model to use for each task. Testing prompts, comparing outputs, switching providers. It worked but it did not scale. So we built a feedback loop. Every request gets traced with input, output, model, tokens, cost, latency, and a quality score. The router clusters similar requests using embeddings and learns which model actually performs best for each cluster. Not based on benchmarks. Based on real production results. After three weeks of traces we had enough validated data to fine-tune a 7B on our workloads.