Adapting large language models (LLMs) to specific, high-stakes domains like law, medicine, or cloud security is often slow, costly, and hard to replicate. Teams spend weeks guessing which approach works best—whether it’s retrieval-augmented generation (RAG), fine-tuning, or other methods—and then tuning hyperparameters without clear guidance. This manual process can lead to inconsistent results and delays,










