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The age of one-size-fits-all AI appears to be crumbling. As enterprises rush to embed artificial intelligence into their operations, a stark reality has emerged: generic language models, while impressive, often stumble when faced with specialized industry needs.

This limitation is particularly glaring for those of us who work in sectors such as voice AI, where our tech is the first step in a complex chain of understanding and action. Converting speech to text perfectly means nothing if the AI can’t grasp industry-specific jargon or generate contextually appropriate responses. Working in the medical space recently, we’ve seen how mixing precise speech recognition with specialty LLMs can mean the difference between accurate diagnosis transcription and potentially dangerous errors.

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