Voice AI for Indian languages lets an AI receptionist understand and answer callers in languages such as Marathi and Hindi, including the mixed speech many people use on the phone. For local businesses, it means customers can ask for what they need in the language they are most comfortable with, instead of being pushed into English or a menu.
India calls in many languages
India has 22 languages in its constitution’s eighth schedule, and Marathi alone has more than 80 million native speakers according to the 2011 census. A neighbourhood clinic, salon, garage or coaching centre in Maharashtra may take calls in Marathi in the morning, Hindi at midday and English in the evening, sometimes within the same sentence.
Most AI receptionists were built and tested in English first. A business in Pune, Nashik or Nagpur can have a polished English receptionist and still lose callers who would rather speak Marathi.
What makes Indian-language voice AI hard
Voice AI has to do two things at once: understand speech and produce natural speech. Indian languages add several challenges:
- Code-mixing. Callers switch between languages mid-sentence, such as Marathi with English words for “appointment” or “booking”.
- Dialects and accents. Marathi spoken in different regions sounds different.
- Names, addresses and numbers. Place names, surnames and phone numbers spoken in regional pronunciations are easy to mishear.
- Phone-line quality. Calls often come from busy streets, with background noise.
- Less training data. Regional languages have fewer public speech datasets than English.
This is why a regional model matters. A model designed for Indian languages handles these problems better than a general English-first model, although accuracy still needs checking with real calls.
The models available
On GMB Briefcase’s AI receptionist, agencies can choose OpenAI, Google Gemini, Sarvam for Indian vernacular languages, and proprietary Marathi voice models. They can also bring their own API key or use the platform defaults. In practice, that means one agency can run an English receptionist for one client and a Marathi-first receptionist for another, rather than forcing every business into the same setup.
What it changes for local customers
A receptionist that answers in the caller’s own language removes a quiet barrier. Older customers, and anyone calling for a family member, are more likely to stay on the line. Questions about prices and timings, which are exactly what local businesses are asked every day, can be answered in a few seconds without waiting for someone to return from the shop floor.
For local SEO, the effect compounds. A business that appears on Google Maps in a regional market gets calls from regional-language speakers. If the phone is answered in the right language, the ranking produces a booking. If it is not answered at all, the competitor wins.
How to test a regional-language receptionist
Do not rely on a demo. Check it against calls that look like the business’s real ones:
- Record or script ten calls in the target language, including mixed-language speech.
- Include phone numbers, locality names and personal names, since these cause the most errors.
- Test with different speakers and a noisy background.
- Check how it handles a question it cannot answer. It should say so and take a message.
- Have a native speaker review the tone. A correct but stiff reply still puts callers off.
Practical tips for agencies
- Ask clients which languages their customers use. Check call recordings or staff, not assumptions.
- Write the knowledge base in the language callers use. Translating after the fact often misses local terms.
- Set a fallback. Decide who a caller reaches if the receptionist cannot follow the conversation.
- Be open about the assistant. Callers should know they are speaking to an automated receptionist.
- Review transcripts in the first weeks, with a fluent speaker if possible.
FAQ
Can an AI receptionist answer calls in Marathi? Yes, where the platform offers a Marathi voice model. GMB Briefcase includes proprietary Marathi voice models alongside Sarvam for other Indian languages.
Does it handle mixed language speech? Models built for Indian languages are designed to handle code-mixing, but accuracy depends on the model and the call, so test with real examples.
Do I need a separate setup for each language? Not necessarily. Many setups let you choose a model per receptionist, so different clients can use different languages.
