BOAR is an offline-first mobile AI system exploring adaptive model routing, local retrieval, selective verification, and resource-aware inference. It chooses how to spend limited device compute based on the task, rather than relying on a fixed model or an always-on multi-model pipeline — built on local LLM inference and local hybrid retrieval (SQLite FTS5 + on-device embeddings).