When a vendor pitches 95% word-error-rate accuracy on an English benchmark, nod politely — then ask how it performs on a call where the customer starts in Hindi, slips into Hinglish, uses an English brand name, and ends with a Tamil expression of frustration. That's Tuesday at any e-commerce call centre in India.
The dominant approach to multilingual voice AI is to run separate models per language and switch between them. The catch: you have to detect the language first, which costs latency, and mid-sentence code-switches break the detection entirely. The caller gets confused; the agent asks them to 'please repeat in Hindi'; the call ends poorly.
Artic's approach is different. Instead of routing between language models, we fine-tune a single model on an Indian conversational corpus that treats code-switching as a first-class input pattern — not an exception. The model never needs to 'switch modes.' It simply understands the way real Indians actually speak.
The business impact is immediate. Our customers in Tier-2 and Tier-3 cities see significantly higher call completion rates compared to English-only or mono-lingual agents. A caller in Coimbatore who hears a confident response in Tanglish trusts the agent. A caller in Lucknow who gets Hindi laced with the occasional English product name doesn't feel like they're talking to a foreign system.
If you're evaluating voice AI for India, stop benchmarking on clean English audio. Build a test set from your actual call recordings — with all the glorious linguistic mess — and see which platform survives. We think you'll find the answer matters more than the accuracy number.