Indic-native LLMs (Sarvam 105B)
Purpose-built for Indian languages — not English models with translation layers. Vocabulary, grammar, idioms, and code-switching are handled natively, tuned for Indic benchmarks.
Build for Bharat, not just English-speaking India. CallMissed's Indic-native AI handles 22 scheduled Indian languages across chat, voice, speech-to-text, and text-to-speech — with code-switching built in.

Vernacular AI isn't about translation — it's about native models trained on Indic speech and text.
Purpose-built for Indian languages — not English models with translation layers. Vocabulary, grammar, idioms, and code-switching are handled natively, tuned for Indic benchmarks.
95%+ WER accuracy on Hindi, 90%+ on Tamil/Telugu/Bengali, 85%+ on smaller languages. Streaming WebSocket under 400ms. Outperforms Google STT on Indian accents by 15–25%.
Each language has 2–6 voices (male, female, child, neutral) trained on professional voice actors. Natural prosody — not the robotic 2015-era Google Hindi voice.
Real customers mix Hindi and English in one sentence. Our models parse mixed-script text, transcribe mixed speech, and respond in the same code-switched style — the only way to sound natural to Bharat.
Devanagari, Tamil, Telugu, Bengali, Malayalam, Gurmukhi, Ol Chiki, Meitei Mayek — every major Indic script handled correctly, including conjunct ligatures and diacritics that break most NLP pipelines.
Hindi in Bihar sounds different from Hindi in Mumbai. Our models are trained across geographic variants so a Bihari farmer and a Mumbai professional both get accurate transcription and natural-sounding replies.
The 22 constitutional languages of India are 1.4 billion speakers strong. Every one of them is supported across LLM, STT, and TTS.
| Script | Language | Sample: "Your order arrives tomorrow." | Speakers |
|---|---|---|---|
| हिन्दी | Hindi | नमस्ते, आपका ऑर्डर कल पहुँचेगा। | 600M+ |
| English (IN) | Indian English | Please check the status of order number 45231. | 200M+ |
| বাংলা | Bengali | আপনার অর্ডার আগামীকাল পৌঁছে যাবে। | 265M+ |
| తెలుగు | Telugu | మీ ఆర్డర్ రేపు చేరుతుంది. | 95M+ |
| मराठी | Marathi | तुमची ऑर्डर उद्या पोहोचेल. | 90M+ |
| தமிழ் | Tamil | உங்கள் ஆர்டர் நாளை வரும். | 85M+ |
| اُردُو | Urdu | آپ کا آرڈر کل پہنچ جائے گا۔ | 70M+ |
| ગુજરાતી | Gujarati | તમારો ઓર્ડર આવતીકાલે આવી જશે. | 60M+ |
| ಕನ್ನಡ | Kannada | ನಿಮ್ಮ ಆರ್ಡರ್ ನಾಳೆ ತಲುಪುತ್ತದೆ. | 55M+ |
| ଓଡ଼ିଆ | Odia | ଆପଣଙ୍କ ଅର୍ଡର କାଲି ପହଞ୍ଚିବ। | 40M+ |
| മലയാളം | Malayalam | നിങ്ങളുടെ ഓർഡർ നാളെ എത്തും. | 35M+ |
| ਪੰਜਾਬੀ | Punjabi | ਤੁਹਾਡਾ ਆਰਡਰ ਕੱਲ੍ਹ ਪਹੁੰਚ ਜਾਵੇਗਾ। | 30M+ |
| অসমীয়া | Assamese | আপোনাৰ অৰ্ডাৰ কাইলৈ আহি পাব। | 15M+ |
| मैथिली | Maithili | अहाँक ऑर्डर काल्हि पहुँचत। | 13M+ |
| संस्कृतम् | Sanskrit | भवतः आदेशः श्वः प्राप्स्यते। | Heritage |
| कॉशुर | Kashmiri | تۄہنٛد آرڈر پگاہ پؠچن۔ | 7M+ |
| कोंकणी | Konkani | तुमची ऑर्डर फाल्यां पावतली. | 2.5M+ |
| सिन्धी | Sindhi | توهان جو آرڊر سڀاڻي پهچندو. | 2.5M+ |
| डोगरी | Dogri | तुसदा ऑर्डर कल्ल पुज्जग। | 2M+ |
| ꯃꯅꯤꯄꯨꯔꯤ | Manipuri | নখোয়গী অর্ডর হয়েং য়ৌরগনি। | 1.8M+ |
| बड़ो | Bodo | नोंथांनि अर्डार गाबोन सौगौन। | 1.5M+ |
| ᱥᱟᱱᱛᱟᱲᱤ | Santali | ᱟᱢᱟᱜ ᱟᱨᱰᱟᱨ ᱜᱟᱯᱟ ᱥᱮᱸᱫᱽᱨᱟᱭᱚᱜᱼᱟ | 7M+ |
From e-commerce to healthcare, real deployments reaching Bharat.

Most D2C brands in India lose customers because their post-purchase flow is English-only. Switch to vernacular AI support on WhatsApp and voice — customers in tier-2 / tier-3 cities finally get served in their language.
Result
30% higher repeat purchase rate from vernacular markets.

Patients in rural India speak Bhojpuri, Marathi, Odia — not English. AI agents give medication reminders, schedule follow-ups, and collect symptoms in the patient's mother tongue — then translate to English for the doctor.
Result
Doctor consultation throughput up 40%.

NEP 2020 mandates regional-language education. EdTech platforms use CallMissed STT to caption English lessons into 22 Indian languages, TTS to read out course material, and LLM to answer student questions in the student's language.
Result
22-language accessibility without hiring 22 teams.

Government portals (DigiLocker, UMANG, state portals) use CallMissed to answer citizen queries about schemes, eligibility, and application status in the citizen's preferred language — essential for a country where only 10% speak English.
Result
G2C service accessibility to 1.4B citizens.

Banks and fintech reach customers in villages who can't read smartphones. AI voice agents call in the customer's language, explain products, take applications, and walk them through KYC — all voice-driven, no typing.
Result
Rural customer acquisition cost drops 60%.

Last-mile delivery drivers and cab drivers speak regional languages. Voice navigation in their mother tongue reduces errors, accidents from screen-glancing, and failed deliveries.
Result
Delivery completion rate up 12% in tier-3 cities.
Switch languages, voices, and models with a single parameter. Here's a Python example that handles a Tamil voice call end-to-end:
from openai import OpenAI
from callmissed import VoiceAgent
agent = VoiceAgent(
api_key="cm_your_key",
config={
"stt": {"model": "saaras:v3", "language": "ta"}, # Tamil STT
"llm": {"model": "sarvam-105b", "system_prompt": "நீங்கள் Acme Electronics-இன் ஆதரவு முகவர்."},
"tts": {"model": "bulbul:v3", "voice": "kavitha"}, # Tamil female voice
"telephony": {"provider": "exotel", "number": "+91-44-1234-5678"},
},
)
agent.start() # listens on the configured phone number
# That's it. A fully vernacular Tamil voice agent, production-ready.Full voice pipeline in Tamil — STT → LLM → TTS
Because translation loses the speaker. A customer saying "Bhai yeh order cancel karna hai urgent" is not the same sentiment as the literal English translation. Indian-language AI models (like Sarvam M / 105B used by CallMissed) are trained natively on Indic speech and text — they capture tone, context, politeness levels (tu/tum/aap), and cultural idioms that translation pipelines discard.
Your customers don't speak English. Your AI shouldn't have to force them to.