Indian Language AI

The Challenge of Indian Languages

India has 22 officially recognized languages written in 13 different scripts, spoken by 1.4 billion people. Yet most AI systems are built for English first and treat Indian languages as an afterthought — using translated training data, applying English-centric phonetic models, and ignoring the code-switching (mixing two languages in one sentence) that's natural in Indian communication.

The result? Speech recognition that can't handle a Mumbai taxi driver's Hinglish. Chatbots that respond to Tamil queries in English. Voice assistants that pronounce Indian names wrong. For businesses serving Indian customers, these failures cost revenue and trust.

CallMissed solves this with AI models built for Indian languages from the ground up — not adapted from English. Our platform, powered by Sarvam AI's Indic-native models, delivers speech recognition, text-to-speech, and language understanding that works the way Indians actually speak.

Supported Languages

Our platform supports 22+ Indian languages across all three AI modalities (STT, TTS, LLM):

Full Support (STT + TTS + LLM)

• Hindi (hi-IN) — 600M+ speakers. Full Devanagari script support, Hinglish code-mixing, regional dialect handling.
• Tamil (ta-IN) — 80M+ speakers. Native Tamil script, Tanglish handling, formal and colloquial registers.
• Telugu (te-IN) — 85M+ speakers. Telugu script, regional accent variation, Tenglish code-mixing.
• Bengali (bn-IN) — 100M+ speakers. Bangla script, Kolkata and Dhaka dialect awareness.
• Marathi (mr-IN) — 85M+ speakers. Devanagari script, Mumbai/Pune accent handling.
• Kannada (kn-IN) — 45M+ speakers. Kannada script, Bangalore urban accent support.
• Malayalam (ml-IN) — 38M+ speakers. Malayalam script, Kerala dialect patterns.
• Gujarati (gu-IN) — 55M+ speakers. Gujarati script, business communication style.
• Punjabi (pa-IN) — 35M+ speakers. Gurmukhi script, Shahmukhi awareness.

STT + LLM Support

• Odia, Assamese, Urdu, Sindhi, Konkani, Dogri, Maithili, Bodo, Santhali, Kashmiri, Nepali, Manipuri, Sanskrit

English (Indian)

Full support for Indian English (en-IN) — including the specific accent patterns, vocabulary (prepone, revert, do the needful), and code-mixing behaviors unique to Indian English speakers.

Why Indian-First AI Matters

Code-Switching is Natural

Over 50% of urban Indians regularly mix languages in conversation. A customer might say "Mujhe ek delivery reschedule karna hai, yeh order number hai 12345." Generic STT models either transcribe this as garbled Hindi or broken English. Our models understand code-switching natively — transcribing each word in the language it was spoken in, with accurate script rendering.

Accents Vary Dramatically

Hindi spoken in Delhi sounds different from Hindi in Indore or Lucknow. Tamil spoken in Chennai differs from Coimbatore Tamil. Our ASR models are trained on diverse regional accent data, not just standard broadcast speech. This means accurate transcription for real-world audio — call center recordings, street-level conversations, and noisy environments.

Script Matters

Indian languages use distinct scripts — Devanagari (Hindi, Marathi), Tamil script, Telugu script, Bangla script, Kannada script, and more. Our STT produces output in the correct native script, and our TTS reads native script input naturally. Our transliteration mode converts between scripts when needed.

Cultural Context

Our LLMs understand Indian cultural context — festivals, naming conventions, familial titles (ji, bhai, didi, anna), and communication norms. When a customer says "Diwali ke baad delivery karwana hai," the model understands the cultural reference and responds appropriately.

Technology Stack

Speech-to-Text (saaras:v3)

Sarvam's saaras:v3 is the most accurate ASR model for Indian languages. Key capabilities:

• 5 output modes: transcribe, translate, verbatim, transliterate, code-mix
• Auto language detection across 22+ languages
• Speaker diarization for multi-speaker audio
• Robust against background noise, telephony compression, and varied audio quality

Text-to-Speech (bulbul:v3)

Sarvam's bulbul:v3 produces natural-sounding speech that reflects Indian speaking patterns:

• 39 voices across 11 languages with distinct personalities
• Telephony-optimized 8kHz output for voice calls
• Sub-200ms first-chunk latency for real-time applications
• Correct prosody, intonation, and rhythm for each language

Large Language Models

• Sarvam 30B: 30B MoE, 64K context, 2.4B active params. Optimized for real-time Indic language understanding and generation. Best for chatbots, voice agents, and interactive applications.
• Sarvam 105B: 105B MoE, 128K context. Flagship model for complex reasoning, agentic tasks, and nuanced Indic language generation. Best for content creation, analysis, and enterprise use cases.
• 300+ global models: Access GPT, Claude, Gemini, Grok, and more via OpenRouter. Use Indian-optimized models for Indic queries and global models for English-heavy tasks.

Use Cases

• Government services: Voice-based citizen services in regional languages — ration card status, pension inquiries, scheme eligibility in the citizen's mother tongue
• Rural healthcare: Health information and appointment scheduling in regional languages for communities with limited English literacy
• Regional e-commerce: Product discovery, order management, and customer support in the language your customers think in
• Banking the unbanked: Voice-based banking services for customers who can't read English interfaces but can speak their native language
• Education: Study material, tutoring, and exam preparation in regional medium languages

Integration

All our Indian language AI capabilities are accessible through OpenAI-compatible REST APIs. No ML expertise, GPU infrastructure, or model training required. Just an API key and a few lines of code.

Start free with $5 in credits, or explore the documentation to learn more.