Conversational AI in Vernacular Languages: Hindi, Tamil, Telugu, and More

When India crossed the 900-million internet user mark in 2025, a quiet revolution became impossible to ignore: the majority of new users were not communicating in English. They were typing in Hindi on WhatsApp, speaking Tamil to voice assistants, searching in Telugu on Google, and expecting businesses to respond in their language — not the other way around.

Vernacular conversational AI is no longer a nice-to-have feature or a CSR initiative. It is the primary interface through which most Indians will interact with businesses, government services, and digital platforms. This article examines the current state of conversational AI in India's major vernacular languages, the technical progress, the remaining gaps, and what businesses must do to get it right.

The Vernacular Internet: India's Real Digital Economy

English-language internet served India's first 200 million users — the urban, educated, English-comfortable demographic. The next 700 million are fundamentally different. They are:

• Primarily comfortable in one or more Indian languages.
• More likely to use voice than text for complex interactions.
• Accessing the internet primarily through affordable smartphones.
• Located in Tier 2, Tier 3 cities and rural areas.
• Spending significant time on social and messaging platforms, particularly WhatsApp.

For businesses, this means the addressable market for English-only digital experiences has plateaued. Growth comes from vernacular.

Language-by-Language: State of Conversational AI

Hindi

As India's most widely spoken language (over 600 million speakers including second-language speakers), Hindi has received the most attention from AI researchers and platform providers.

• NLU quality: Excellent. Hindi intent classification and entity extraction match English accuracy levels on well-trained systems.
• ASR quality: Very good for standard Hindi. Performance dips with heavy dialectal variation (Bhojpuri-influenced Hindi, Rajasthani-influenced Hindi).
• TTS quality: High. Neural TTS in Hindi sounds natural and is available from multiple providers.
• Code-switching: Hinglish (Hindi-English mix) is well-supported by leading platforms. This is critical since urban Hindi speakers mix English extensively.
• Key challenge: Dialectal diversity. Hindi in UP, Bihar, Rajasthan, and MP varies significantly. Most systems are trained on "standard" Hindi, which may not resonate with users from specific regions.

Tamil

Tamil has a 75-million+ speaker base and strong digital adoption in Tamil Nadu and among the Tamil diaspora.

• NLU quality: Good. Dedicated Tamil NLU models are available, though fewer pre-trained options exist compared to Hindi.
• ASR quality: Good for text-to-speech. Voice recognition in Tamil faces challenges with the language's agglutinative structure — words can be very long, combining multiple morphemes.
• TTS quality: Improving rapidly. Tamil TTS has reached conversational quality with proper intonation.
• Code-switching: Tanglish (Tamil-English) is common, especially among younger urban users. Support is growing but not as mature as Hinglish.
• Key challenge: Formal vs. spoken Tamil divergence is significant. Written Tamil and spoken Tamil are almost different registers of the language. Conversational AI must handle spoken Tamil, which is less standardised.

Telugu

Telugu has over 80 million native speakers, making it one of India's largest language communities, primarily in Andhra Pradesh and Telangana.

• NLU quality: Good and improving. IndicBERT and similar models provide a strong foundation.
• ASR quality: Moderate to good. Telugu ASR benefits from the language's relatively phonetic script, but available training data is less than Hindi.
• TTS quality: Good. Multiple providers offer neural Telugu TTS.
• Code-switching: Telugu-English mixing is common, especially in Hyderabad. Dedicated code-switching models are less common than for Hinglish.
• Key challenge: Two distinct dialects — Telangana Telugu and Andhra Telugu — with significant vocabulary and pronunciation differences. Users notice and care about this distinction.

Bengali

Bengali serves over 100 million speakers across West Bengal and Bangladesh (though this article focuses on the Indian context).

• NLU quality: Good. Bengali benefits from a rich literary tradition that has produced substantial digital text corpora.
• ASR quality: Moderate. Bengali speech recognition is improving but remains behind Hindi in accuracy.
• TTS quality: Good. Bengali TTS with proper intonation is available from major providers.
• Code-switching: Banglish (Bengali-English) is prevalent in Kolkata and among younger speakers.
• Key challenge: Nasalisation and tonal nuances in Bengali speech can affect ASR accuracy. Also, the Bangla script has complex conjuncts that complicate text processing.

Marathi

Marathi, with 83+ million speakers primarily in Maharashtra, is critical for businesses operating in India's financial capital.

• NLU quality: Good. Marathi benefits from its similarity to Hindi in grammar and vocabulary, enabling some transfer learning.
• ASR quality: Moderate. Fewer dedicated Marathi ASR models exist compared to Hindi.
• TTS quality: Good and improving.
• Key challenge: Marathi has significant urban-rural linguistic variation. Mumbai Marathi differs substantially from Vidarbha or Konkan Marathi.

Kannada, Malayalam, Gujarati, and Others

Each of India's other major languages has its own trajectory in conversational AI. Generally:

• Dravidian languages (Kannada, Malayalam) benefit from strong academic NLP research communities.
• Gujarati benefits from business demand driven by Gujarat's commercial activity.
• Punjabi, Odia, and Assamese are in earlier stages with fewer commercial deployments but growing investment.

Technical Approaches to Vernacular Conversational AI

Approach 1: Translate-Then-Understand

Translate user input to English, process it through an English NLU pipeline, then translate the response back. This was the default approach for years.

Pros: Leverages mature English NLP. Quick to deploy for multiple languages.

Cons: Translation errors compound. Latency increases. Cultural nuances and code-switching are lost. The experience feels unnatural to users.

Approach 2: Multilingual Models

Use multilingual models (mBERT, XLM-R, IndicBERT) that understand multiple languages natively. Train a single NLU model on data from all target languages.

Pros: No translation step. Handles code-switching naturally. Shared learning across languages improves low-resource language performance.

Cons: Performance may be lower than language-specific models for high-resource languages. Requires training data in each target language.

Approach 3: Language-Specific Models

Build dedicated NLU, ASR, and TTS models for each language. This is the highest-quality approach.

Pros: Best accuracy and naturalness for each language. Can capture dialect-level variations.

Cons: Expensive. Requires separate data, training, and maintenance for each language. Does not scale well to many languages.

Approach 4: Hybrid (Recommended)

Use multilingual models as the foundation, then fine-tune for specific high-priority languages with language-specific data. Use language-specific ASR and TTS (where quality matters most) with multilingual NLU (where the transfer learning benefits are strongest).

This hybrid approach delivers the best balance of quality, cost, and scalability for Indian deployments.

Best Practices for Vernacular Deployment

1. Prioritise by business impact: Start with the languages that serve your largest or most underserved customer segments. For most pan-India businesses, Hindi + English + two regional languages covers 70-80% of users.
2. Collect real vernacular data: Do not rely on translated training data. Collect and label authentic conversations in each target language, including code-switching patterns.
3. Test with native speakers: Automated metrics are necessary but not sufficient. Have native speakers evaluate the AI's language quality, cultural appropriateness, and naturalness.
4. Localise, do not just translate: Responses should feel native to the language, not like translated English. This includes appropriate greetings, honorifics, sentence structure, and cultural references.
5. Support transliterated input: Many users type Indian languages in Latin script (romanised Hindi, romanised Tamil). Your system must handle both native script and transliterated input.
6. Invest in voice: For Tier 2/3 markets, voice is often the primary interaction mode. Ensure your ASR and TTS are high-quality for each target language.
7. Monitor per-language performance: Track accuracy, CSAT, and containment rate separately for each language. Aggregate metrics can hide underperformance in specific languages.

The Bhashini Effect

India's national language technology mission, Bhashini, is playing a significant role in accelerating vernacular AI. By providing open-source ASR models, translation models, and language datasets for all 22 scheduled languages, Bhashini is lowering the barrier for businesses to build vernacular conversational AI. Enterprises should actively leverage Bhashini's resources alongside commercial platforms.

The Business Case

The ROI of vernacular conversational AI is compelling:

• Market expansion: Access the 85% of Indians who prefer interacting in their native language.
• Higher engagement: Users interact 40-60% more with vernacular interfaces compared to English-only alternatives.
• Better conversion: E-commerce platforms report 50-70% higher conversion rates with vernacular shopping assistants.
• Customer loyalty: Being served in one's own language creates an emotional connection that English cannot match for most Indians.

Speaking India's Languages

The future of conversational AI in India is vernacular. Businesses that invest in genuine, high-quality vernacular capabilities — not token translations but truly native language experiences — will capture the next wave of India's digital economy.

AnantaSutra builds conversational AI that speaks India's languages with native fluency. From Hindi to Tamil, Telugu to Bengali, we deliver voice and chat experiences that feel local, natural, and culturally aware. Let us help you speak your customer's language.