Introduction

India is an incredible nation boasting a rich and varied cultural landscape that encompasses a vibrant, deeply rooted linguistic fabric; it embodies the principle of Unity in Diversity, fostering a society where people of different linguistic communities coexist and contribute to the collective national identity. The vastness of languages and dialects is such that it is generally estimated that noticeable changes in language or dialect occur every 50 to 100 kilometers. There is a popular aphorism that illustrates India’s linguistic diversity beautifully – “Kos kos par badle paani, char kos par baani” which translates to “The taste of water changes every kilometer and the language changes every few kilometers”. The reason for such multiplicity of languages among myriad ethnic communities is attributed to the ancient origins of Indian civilization. India is perhaps the only nation that has embraced its invaders—who sought to establish their hegemony and plunder the local assets—yet, fate turned out such that they were enamoured by this country's natural and cultural beauty and subsequently became part of its cultural milieu.

The 2011 linguistic census accounts for 121 mother tongues, including 22 languages listed in the 8th Schedule of the Constitution. Indian Constitution accommodates this linguistic diversity in the form of Article 343, 344 (1), 351 and 347, where apart from Hindi and English as Official Languages, recognition is given to those languages that are not the ‘official language’ of the State, allowing for greater State autonomy.

The large scale diversity in terms of language is an asset for the entire nation that needs to be leveraged for fulfilling the vision of Viksit Bharat 2047 bringing revolutionary transformation across multiple domains like agriculture, health, education, finance, tourism, etc to make the nation a developed one and in this endeavour technology is going to play the pivotal role. Today’s world is making huge strides in the field of technology, especially in Artificial Intelligence which is now the domain which the entire world is vigorously exploring. In this context, it is needless to mention that India is also undergoing a remarkable transformation in Artificial Intelligence. In this year’s AI summit in Paris, the Prime Minister of India, in his address, highlighted the following salient points:

1. It is an undeniable fact that AI is having its ubiquitous presence in the global landscape and is reshaping our polity, economy, security and society.
2. AI should be deployed for the global good and be a facilitating tool for upholding shared values, addressing risks and building trust.
3. Prime Minister advocated for democratisation of technology, especially he was vocal for the Global South with the prime focus on developing and implementing people centric applications so that achieving the Sustainable Development Goals becomes a reality. 

The rich linguistic tapestry of the country can be woven with Artificial Intelligence to produce something which can lead to ground-breaking innovation, making every sector thrive; however, this is not an easy task, as the following aspects have to be kept in mind while devising AI models for the growth and expansion of Indian languages.

Script Diversity: 

While some scripts are more widely used, like Devanagari for Hindi and Sanskrit, others, like Tamil, Telugu, Kannada, Malayalam, and Gujarati, have their own distinct scripts. It is challenging for AI models that can manage different scripts because the internet does not have enough materials in these languages which are therefore known as low resource languages for the model to train on. Therefore, data needs to be created and that is what various Indian start-ups and GOI agencies are doing by collecting data through diverse sources, crowdsourcing, digitization of Government resources, creating domain specific information like compiling medical books in target languages if we are building an AI model for medicine, mass scale translation of materials from English to target languages and gathering audio data in a variety of Indian languages for speech-related AI applications, such as text-to-speech synthesis and speech recognition.

Dialectal Diversity:

There are numerous regional dialects and linguistic variances in Indian languages that need to be considered.

Lesser known languages/ Languages limited to very specific communities: For a large number of India’s lesser known languages, data resources are very little for training the AI model. In these cases, there is a dearth of written records, leave aside digital records and for that reason, creation of AI applications in these languages is a cumbersome task.

Language Preservation: 

By developing digital tools and resources for endangered languages, AI can help promote and preserve them. These languages are being documented and digitalized.

Content Localization: 

Applications and content must be translated into several languages in order to reach a larger audience. Through automation of translation and adaption, AI can help with the localization process.

History shows us that India has always proactively developed a self-reliant ecosystem, be it in food security or nuclear power or developing effective COVID vaccines, such that she efficiently withstands any adverse policy impact thrust upon by the Global North countries. Similarly, India is strongly building its foundation for AI growth that is both inclusive and globally competitive through initiatives like IndiaAI Mission, GPU Access Programs, robust GPU supply chain, affordable compute access (allowing researchers and startups to access GPU power at a highly subsidised rate of ₹100 per hour, compared to the global cost of $2.5 to $3 per hour), strengthening semiconductor manufacturing and indigenous AI Model Development to reduce reliance on imported technology.

AI Dataset Platform: Powering AI Innovation

Let us first understand the basic concept of Large Language Models which are a powerful subset of Natural Language Processing (NLP) central to the working of AI in practical applications. Large language models (LLMs) are a category of foundation models trained on an immense volume of data, making them capable of understanding and generating natural language and other types of content to perform a wide range of tasks. LLMs have been instrumental in bringing Generative AI to the forefront of the public interest as well as for organisations which are focusing on increasing the efficiency of systems and processes by adopting artificial intelligence across business functions and use cases. The beauty of LLMs compared to traditional programming languages is that LLMs make a machine learn to do things, whereas the traditional programming languages explicitly tells the machine how to do things. To facilitate the learning process of the machines, the LLM models need to train on massive data sets. That is why IndiaAI Dataset Platform has been launched by the Government of India to provide seamless access to high-quality, non-personal datasets. This platform will house the largest collection of anonymised data, empowering Indian startups and researchers to develop advanced AI applications. Specialized AI Centres of Excellence (CoE) will focus on key areas such as healthcare, agriculture, sustainable cities, and education. The first three centres were launched in 2023, while this year’s Budget announced an additional centre for AI in education with an outlay of ₹500 crore. In addition, five national centres for AI skilling will be established to train India’s youth with industry-relevant AI expertise. These centres will be set up in collaboration with global partners to support the ‘Make for India, Make for the World’ vision in manufacturing and AI innovation.

Why Multilingual Support Matters?

In India’s context, the need for multilingual support is indispensable. In order to capitalize on the demographic dividend that India currently enjoys, the human development index indicators, v.i.z. Health, Education and Living Standards need to improve on a massive scale and for that Government’s public welfare projects need to penetrate to the last mile. Here comes the role of technology and multilingual support, wedded together to cater to the diverse population of the nation. We have these popular LLMs to build generative language capabilities and these pretrained models like GPT give developers a head start in comparison to training models from scratch. There is a concept called Finetuning in LLM that allows us to take existing foundational models and finetune them with our specific use cases for example, answering questions in native languages on various promotional schemes of the Government for budding sports persons. Fine Tuning allows pretrained models to be finetuned for real world use cases, and it again depends on the quality of the dataset. For example, we can finetune a model to be a one stop guiding solution specifically for the youth of India to choose career centric skills in indigenous languages. In order to have conversations and be able to answer questions on various domains and to choose one’s desired domain of skill it has to train on pre – existing set of back and forth career counselling conversations in various languages between the student and counsellor, subsequently load that in, fine tune the model and all of a sudden the model shall start performing the task of career counselling in a brilliant fashion. Multilingual support can significantly enhance efficiency, accessibility, and user experience across many sectors. Here are key sectors where multilingual support is immensely beneficial.

Education: 

By 2030, India will have the largest number of youth population in the globe, a population size that will be a boon only if these young people are skilled enough to join the workforce. Quality education will play a major role in it and a concrete step in this direction is disseminating education in the mother tongue. “In making English the sole language of intellectual discourse in science and technology in India, we have lost on many fronts”, as was remarked by Prof. K Vijay Raghavan, Former Principal Scientific Adviser to GOI. India a powerhouse of technology, has set on a path of shrugging off the colonial influence and emphasizing on the importance of learning S&T in the mother tongue. This perspective is reflected in one of the key themes of the National Education Policy (NEP)-2020. Students being able to access education in their mother tongue helps in their cognitive development, improved comprehension and learning and in stronger communication skills.

Financial Literacy: 

India’s average financial literacy score (as per RBI data) stands at an abysmal 11.9 out of 100. In order to improve this score, people’s financial knowledge, financial attitude and financial behaviour needs to upgrade. Again, the linguistic diversity provides for a scope to develop financial outreach programmes by financial organisations in an effective manner. LLMs can be used as a tool for explaining complex financial concepts and providing tutorials to customers in their native languages. This not only empowers customers to make better financial decisions but also fosters a stronger relationship of the BFSI sector with customers. AI-powered language solutions have become indispensable in multilingual fintech support:

1. Natural Language Processing (NLP): Chatbots and virtual assistants powered by NLP algorithms facilitate seamless customer interactions. Customers can ask questions, process transactions, and get personalized financial advice without waiting for a representative.
2. Sentiment analysis: Sentiment algorithms analyze customer feedback across languages, helping financial institutions fine-tune their services. As a result, customers across a diverse linguistic landscape enjoy better experiences which in turn helps in fostering trust.
3. Comprehensive Localisation: Translation is not the only job at hand; the user interface also needs to work on ensuring accurate and culturally sensitive translation and textual and spoken conversations.
4. User-centric Design: Adaptation of the user interface to accommodate varying text lengths, character sizes, and right-to-left scripts. Extensive testing should also be conducted to ensure readability and functionality.
5. Compliance: The language models should be tailored to fit in all the legal materials, fintech rules and regulations like KYC and AML requirements, etc.
6. Multilingual Customer Support: Offering multilingual customer support in multiple languages through phone, email, and chatbot is very crucial to spread financial literacy as well as offer proper financial advice to the customers, thereby acting as a facilitator in building a financially strong nation. This will also strengthen the campaign against cyber fraud which is quite rampant across the nation.
7. Testing and Feedback: Conducting thorough testing with native speakers and gathering user feedback to identify and address language-specific issues or cultural nuances and train the language models accordingly.
8. Continuous Improvement: Monitoring multilingual usage and engagement analytics to make data-driven decisions for ongoing improvements and expansion of financial services to remotest areas of the country.
9. Scalability: Building a robust and scalable multilingual framework capable of supporting future language integrations and functional upgrades1.
10. Healthcare: The growth of Indian Languages using AI is going to be utmost beneficial for the common man of India in accessing cost effective healthcare solutions in their own native language. Using the patient's language also fosters a more culturally sensitive and respectful environment, as it recognizes the importance of language in understanding and addressing health concerns.
11. Agriculture: Agriculture still being the mainstay of the Indian economy, language-specific data-driven advisory apps can empower Indian farmers across state boundaries with:
    a. Real-time, tailored insights
    b. Inclusivity regardless of literacy or tech skills
    c. Scalable support for India’s 140+ million farmers delivered across multiple agro–climatic zones.

They are not just a technical solution — they are a social equalizer.

India’s Digital Public Infrastructure (DPI) has transformed digital innovation by combining public funding with private sector-led innovation. Platforms like Aadhaar, UPI, and DigiLocker serve as the foundation, while private entities build application-specific solutions on top of them. This model is now being enhanced with AI, integrating intelligent solutions into financial and governance platforms. The global appeal of India’s DPI was evident at the G20 Summit, where several countries expressed interest in adopting similar frameworks. Japan’s patent grant to India’s UPI payment system further underscores its scalability. AI is being used to enhance India's Digital Public Infrastructure (DPI) by facilitating access to government services and digital platforms in various Indian languages. National Payments Corporation of India (NPCI) today is using these AI-driven language models to provide voice-activated commands in Hindi or English for payments. This has helped Indian users to speak in the language of their choice over the phone, or while using a WhatsApp bot or aggregating information to execute tasks over the phone.

For Mahakumbh 2025, AI-driven DPI solutions played a crucial role in managing the world’s largest human gathering. AI-powered tools monitored real-time railway passenger movement to optimise crowd dispersal in Prayagraj. The Bhashini-powered Kumbh Sah’AI’yak Chatbot enabled voice-based lost-and-found services, real-time translation, and multilingual assistance. Its integration with Indian Railways and UP Police streamlined communication, ensuring swift issue resolution. By harnessing AI and DPI, Mahakumbh 2025 redefined global standards for inclusive and efficient event management powered by cutting-edge technology.

India’s AI Models and Language Technologies

Last year a meeting was held between Prime Minister of India, Shri Narendra Modi and American business tycoon Bill Gates without the presence of the customary interpreters. This was made possible by the use of a home grown AI led language translation platform, BHASHINI, that translated the Prime Minister’s hindi speech in real time for Mr. Gates to comprehend. This was a high profile proof of concept which was a resounding success. The Government of India is facilitating the development of India’s own foundational models, including Large Language Models (LLMs) and problem-specific AI solutions tailored to Indian needs which include the following:

• Digital India BHASHINI: An AI-led language translation platform engineered for seamless internet connectivity and digital services in Indian languages, including voice-based access, and supports multilingual content creation across Indian regional languages. On the lines of Bhashini, Project Vaani is an initiative led by AI4Bharat in collaboration with Google Research and other partners, with an objective to create open datasets and tools to accelerate speech technology for Indian languages. A key target of Project Vaani is to support under–resourced languages.
• BharatGen: The world’s first government-funded multimodal LLM initiative, BharatGen, was launched in 2024 in Delhi. It aims to enhance public service delivery and citizen engagement through foundational models in language, speech, and computer vision. BharatGen involves a consortium of AI researchers from premier academic institutions in India.
• Sarvam-1 AI Model: Sarvam-1 is a large-scale language model optimized specifically for Indian languages, featuring 2 billion parameters and support for ten major Indian languages. The model is architected to facilitate advanced natural language processing tasks such as machine translation, abstractive text summarization, and generative content creation across diverse linguistic contexts.
• Chitralekha: Chitralekha is an open-source video transcreation platform developed by AI4Bhārat that enables seamless generation, editing, and localization of audio transcripts across multiple Indic languages. It facilitates accurate video content adaptation by integrating speech recognition, natural language processing, and multilingual text-to-speech technologies.
• Hanooman’s Everest 1.0: A multilingual AI system developed by SML, Everest 1.0 supports 35 Indian languages with a roadmap to extend support to 90 languages.
• Jugalbandi: Simulating the performance of two vocal soloists on equal footing in Indian Classical Music, India has launched it’s very own AI-based chatbot that can answer questions on the government’s welfare schemes in several Indian Languages.

Challenges and way forward

Pre-processing and data collection are essential phases in the creation of AI models for Indian languages. As already mentioned this is a gargantuan task, and all stakeholders’ proactive contribution is highly solicited in this exercise. Crowdsourcing, which has been referred to at the beginning is proving to be an effective tool that has stimulated the interest of the workers in the process of collecting speech and language data in India as it has attached economic value with speech data – an hour of Odia speech data used to cost about $3-$4, now the same is priced at $40. This is basically community involvement. The creation of reliable and contextually aware AI models for Indian languages would be crucial as AI technology develops with time. While a dedicated AI-specific law is still under development, India has implemented a range of measures to address AI-related concerns. Existing laws, such as the IT Act, the Digital Personal Data Protection Act (DPDP Act), and related rules, provide a framework for AI-related activities. The government has also issued advisories and guidelines to ensure responsible AI development and deployment.

Conclusion

With significant ramifications for India's linguistic and cultural diversity, artificial intelligence in Indian languages is a vibrant and rapidly developing topic which is going to revolutionize India’s development story in the near future and shall open a plethora of avenues for every citizen of the nation by bridging the language divide. In fact breaking the language barrier will usher in transformative growth and dismantle long-standing economic barriers. With a clear vision for the future, India is all set to become a leader in AI innovation, shaping the global AI landscape in the years to come, and in the process, this will empower every Indian by providing equitable access to information, services and opportunities, thus making "every voice heard, every dream supported".     

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