LLM assistants deployed outside the United States often inherit US-centred interactional defaults. We introduce an Australian-facing assistant, and test whether local voice is better learned during fine-tuning or imposed with an inference-time prompt. We define Australian Voice as pragmatic alignment, directness, modesty, warmth, register control, and task fidelity, rather than slang or spelling substitution. We build a governed dataset from user research, human rewrites, contrastive negatives, reviewed synthetic examples, and replay data. In a blind evaluation by Australian English users (356 question-level ratings over 89 prompts), the raw 50% voice/replay mix scores best on all five dimensions (4.24/5; 102 best-pick wins). Prompted variants underperform every matched fine-tuned model.