How India is Building Indigenous Multilingual Generative AI Models: BharatGen, Krutrim & More

Introduction

Hook: A short anecdote / statistic about how many Indians don’t use tools well because of language barriers.
Define “multilingual generative AI models” and why “indigenous / local-language” models are important.
Brief overview: mention how India has 22 scheduled languages, many dialects, huge linguistic diversity.
Purpose of the article: explore what Indian projects exist, what challenges they face, what opportunities lie ahead.

A. Government Initiatives

BharatGen
- Launched under NM-ICPS, led by IIT Bombay.
- Aims: multimodal (text, speech, vision), support for all 22 scheduled Indian languages by mid-2026.
- Key features: data efficient learning, focus on underserved languages, open-source, “public good” orientation.

B. Startup / Private Sector Models

Krutrim LLM
- Multilingual foundational model built for a large population, addresses data scarcity, balanced performance across dialects.
Sarvam AI
- Voice-enabled, multilingual platform; supports ~10 languages; full-stack GenAI applications; among sectors like finance, legal, consumer goods.
BharatGPT (by CoRover.ai)
- Multi-lingual text/voice/video; designed for enterprise virtual assistants. BharatGPT
Aya by Cohere for AI
- A model covering 101 languages, including multiple Indic languages; good benchmark performance.

C. Tools / Niche Applications

PixelYatra by Appy Pie (Hindi AI design tool) — generative visual content through Hindi prompts.
Hanooman – free generative assistant for many languages incl. Indian ones.

: Technical & Logistical Challenges

–Linguistic inclusion: ensuring minority languages are not left behind.

Many Indian languages/dialects have minimal digitized text or data; English dominates large corpora.

Example: languages like Bodo, Santali, etc. are under-represented.

–Non-standardized Scripts / Code-Switching / Dialects:

Spelling / orthography variations, script overlaps (e.g. Bengali vs Assamese), code-mixing (Hindi + English etc.).

-Data collection & curation:

Need for curated, high-quality datasets; issues of bias; privacy & consent.

Synthetic data generation to supplement real data (e.g. recent work like Updesh dataset across 13 Indian languages) to boost low-resource performance.

–Compute & Infrastructure Constraints:

Training multimodal or large multilingual models require compute, storage, data pipelines.

Also on-device / edge inference constraints: latency, memory, network connectivity in rural areas.

–Evaluation & Benchmarking:

Need for benchmarks tailored to Indian languages; many global benchmarks focus on English or a few major languages.

Human evaluation needed for fluency, cultural appropriateness.

–Ethical, Cultural, Social & Legal Issues:

Bias in datasets, cultural mis-representation.

Data sovereignty (who owns the data), consent, privacy.

: Opportunities & Use Cases

Education & Learning
- Local-language tutors, language learning tools, translation/localization of curriculum.
- Helping students in remote areas with content in their mother tongue.
Healthcare & Telemedicine
- AI tools that understand / communicate in local languages; translation of medical instructions; voice-based diagnostic assistants.
Governance & Public Services
- E-governance portals, local complaint/grievance systems, translating government announcements in many languages.
Content Creation & Media
- Creators using local languages; vernacular content for social media, entertainment, visual content tools like PixelYatra.
Business / Enterprise Use
- Multilingual agents for customer support, chatbots, business automation.
- Voice UI, local language interfaces as competitive advantage in vast Indian market.
Cultural Preservation
- Digitization of folklore, literature, oral traditions; ensuring languages & dialects survive.

Key Projects & Models to Watch / Compare

Model / Project	Number of Languages Supported	Special Features	Strengths	Limitations
BharatGen	9 (now) → 22 scheduled by mid-2026	Multimodal (text, speech, vision), data-efficient learning, open-source, public good mandate.	Strong government backing; covers many languages; interoperability across modalities.	Being built; some languages still upcoming; quality in less-represented languages yet to be proven.
Krutrim LLM	Multiple Indic languages + English	Large token count, balanced performance; good benchmark scores.	Strong performance; good for mixed/dialect speech.	Complexity; resource needs; generalization to dialects or rare languages may lag.
Sarvam AI	~10-11 languages; voice-enabled	Full-stack GenAI, agents over voice & text; emphasis on voice & ease of deployment.	Voice capabilities; practical use in apps; faster development cycles.	Edge cases in dialects; resource constraints; quality variation.
Aya (Cohere)	101 languages globally incl. many Indian ones	High multilingual benchmarks; instruction following; broad research & open source orientation.	Strong evaluation; access to global research community; multilingual reach.	Might not be as optimized for India-specific cultural context; possible trade-off between breadth and depth.

Paths Forward / What Needs to Happen

Sustainable models for updating & maintaining datasets & models.

Stronger Data Ecosystems

More collection of high quality textual, speech, image data in regional languages & dialects.

Partnerships with local communities, universities, NGOs for digitization and annotation.

Improved Model Architectures

Models that are efficient, can run well on limited compute / edge devices.

Architectures that handle code-switching, script variations, dialectal shifts.

Better Benchmarks & Evaluation Metrics

Benchmarks that include Indian languages, dialects, cultural relevance.

Human evaluation especially for nuance, style, tone.

Policy, Regulation & Ethical Guidelines

Ensuring privacy, data protection, consent, fairness.

Support for open source / public good models so access is not limited to only big players.

Investment & Compute Infrastructure

Access to GPUs, cloud, edge compute; incentives for local AI infrastructure.

Government, academia, industry collaboration.

Inclusion of Minority Languages

Not just major regional languages, but “low-resource” and endangered ones.

What This Means for Global Audience & India’s Position

India as a case study for multilingual AI: lessons for other diverse-language countries.
Potential export of models / tools / research to other countries with similar linguistic diversity.
Strategic importance: technological sovereignty (“Make in India” / “Atmanirbhar Bharat”).