Building multilingual support bots: why language is a routing problem, not translation

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A software team building AI support bots for Southeast Asian businesses found that multilingual deployment is actually three distinct problems: detecting code-switching, matching model size to language complexity, and routing within a single conversation turn. They discovered that data residency regulations like Singapore's PDPA and low-latency requirements pointed to the same architectural solution — running inference in-region on locally hosted infrastructure. Rather than relying on a single large model, the team routes queries across 25+ models behind one unified endpoint, using smaller models for English and larger ones for lower-resource languages like Malay or Tamil, which significantly reduced costs. A key blind spot emerged in monitoring: a single aggregate quality score can mask drift in low-resource languages for days until a customer complaint surfaces, prompting a shift to per-language Macro-F1 tracking.

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