Indian tech companies are intensifying efforts to develop artificial intelligence models for local languages, but face a critical bottleneck: scarcity of quality training data in Indic scripts. This challenge has become the defining factor in making AI truly accessible to India’s 1.4 billion people, where over 22 official languages and 1,600 dialects create one of the world’s most complex linguistic landscapes.
“The primary obstacle in developing Indian language models is the accessibility of data—specifically, high-quality, refined data,” explains Professor Pushpak Bhattacharyya from IIT Mumbai, a leading authority on AI applications for Indian languages. While gathering data in widely spoken languages like Hindi and English is straightforward, acquiring it for other tongues proves far more complex.
Soket Labs founder Abishek Upperwal describes the challenge: “This includes the Common Crawl Foundation, which is a repository of web crawl data, but this does not have 100% of the internet, and Indic language data is missing as well.” Companies are resorting to creative solutions, including licensing content from publishing houses for languages like Gujarati and Urdu, and crowdsourcing voice donations from communities.
Script Complexity Amplifies Challenges
Beyond data scarcity, Indic languages use abugida scripts where letters are merged together, unlike English’s distinct alphabet system. Kaushik Gopalan from FLAME University explains that “algorithms and Unicode systems are oriented towards alphabets, not abugida,” making AI development inherently more challenging.
Research by Shagun Dwivedi reveals that English-centric tokenizing algorithms lead to poor downstream performance in Hindi and prove less computationally efficient, highlighting how fundamental AI architectures favor alphabetic scripts over Indic systems.
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Corporate Innovation Despite Hurdles
Leading startups are pioneering diverse approaches to overcome these limitations. Gnani.ai is working with linguistic organizations across Telugu, Hindi, Tamil, and Marathi while crowdsourcing Indic language content. BharatGPT leverages client data after obtaining permission, while NaaV AI partners with Sarvam for translation capabilities.
Krutrim AI, India’s first AI unicorn valued at $1 billion, represents the massive investor confidence in Indic language models. Similarly, Sarvam AI’s $41 million funding and Lightspeed backing demonstrate growing belief in these specialized solutions.
Government Leadership Through Bhashini
The government-backed Bhashini initiative, launched in 2022, has emerged as a crucial catalyst, already hosting 350 AI-based language models that have managed over a billion tasks. Amitabh Nag, CEO of Digital India’s Bhashini Division, envisions that “in the next two to three years, rural populations will gain voice-enabled access to government services in their native tongues”.
Bhashini collaborates with over 50 government departments and 25 state governments, powering multilingual chatbots for public services and translating government programs into regional languages, ensuring India’s linguistic identities are represented rather than depending on global solutions.
Enterprise Adoption Reveals Market Demand
The Greyhound CIO Pulse 2025 survey found that 67% of enterprises exploring Indic LLMs report frequent failures in multilingual tasks, underscoring both demand and current limitations. However, success stories are emerging: an AgriTech startup in Rajkot reported that training customer care chatbots in Gujarati using Bhashini APIs resulted in enthusiastic rural user adoption.
In Kerala, AI reading tools for dyslexic children now operate in Malayalam, helping thousands access education more effectively, while news channels use Bhashini to generate real-time subtitles for regional YouTube broadcasts.
The Inclusion Imperative
“Without technology that comprehends and communicates in these languages, millions are left out of digital transformation, particularly in education, governance, healthcare, and banking,” warns Professor Bhattacharyya. The stakes extend beyond convenience—this represents India’s fight against digital exclusion.
Vivekananda Pani from Reverie Language Technologies cautions that while AI translation facilitates communication, “there is a risk of less prevalent dialects being marginalized,” requiring careful balance between technological progress and linguistic diversity preservation.
The challenge isn’t merely technical—it’s about ensuring AI serves India’s entire population rather than just urban English speakers, making 2025 a pivotal year for determining whether technology amplifies or reduces India’s linguistic divide.
