Listen to an actual customer service call in most of India and it doesn't stay neatly in one language. Someone opens in Hindi, switches to English mid-sentence for a specific term, drops back into Hindi, maybe throws in a Marathi word if that's their first language. This isn't broken speech. It's how a huge share of the country actually talks, and it's a genuinely different problem from simply supporting multiple languages.
Why this trips up a lot of voice AI
Supporting sixteen languages sounds like a straightforward checklist item: get the model to understand and speak each one. Code-switching, mixing languages within a single sentence or conversation, isn't solved by that checklist. It requires the system to track which language someone is speaking at any given moment and adapt on the fly, without waiting for the caller to finish a whole sentence in one language before figuring out what's being said.
A system built for single-language turns, expecting each utterance to be cleanly in one language, will often mishear or mistranscribe the moment a caller switches mid-thought. That's not a minor accuracy dip. It's the difference between an agent that understands the caller and one that's visibly confused by completely normal speech.
Where CallUltra actually stands on this, honestly
We support 16 languages across 30 voice personas, and language switching within a call is handled through prompting the underlying model to follow the caller's lead. That works for a meaningful share of real conversations. It's a different approach, and a less specialized one, than platforms that have built dedicated models trained specifically on code-mixed speech data, which handle rapid mid-sentence switching more reliably.
We'd rather say that plainly than let a features list imply parity with a purpose-built solution. If your business runs almost entirely on Hindi-English code-switched calls at high volume, particularly for something like collections or verification where accuracy on every word matters, it's worth testing multiple platforms specifically on that scenario, including ones that specialize in it, before deciding.
What tends to work well regardless
Calls that stay mostly in one language, even if a caller occasionally drops in a single word from another, tend to go smoothly across most platforms including ours. It's specifically the rapid, sustained switching mid-sentence that separates general-purpose language support from something purpose-built for it.
How to actually evaluate this for your use case
Don't test with a scripted sentence in one language. Call and speak the way your actual customers speak, mixing languages the way they naturally would, and see what the agent does with it. If it asks you to repeat yourself, or clearly mishears the switched-in words, that's real signal about whether a platform's language support matches the way your callers actually talk versus how a features page describes it.