Beyond Sarvam: Where India's AI opportunity lies beyond language models

Sarvam's success shows that India can build foundational AI infrastructure. The bigger opportunity may lie in domain-specific AI built for Indian realities and emerging markets

As India accelerates its artificial intelligence (AI) ambitions, much of the spotlight has fallen on homegrown large language models (LLMs). Sarvam has emerged as a standout example, raising $234 million in its Series B funding round and pushing its post-money valuation to $1.5 billion.

 

But Sarvam’s success also advances the conversation around India’s sovereign AI story. It shows that India can build a full-stack AI platform. However, experts say the country’s larger AI opportunity may not lie only in building models that can answer prompts. It may lie in building systems that understand local languages, sector-specific data, public-service workflows, cost constraints and the realities of large-scale deployment.

 

 

Where do opportunities in AI lie beyond language models?

 

According to Vivek Prakash, CEO of AI and coding education platform Codingal, healthcare, agriculture and education are some of the biggest opportunities in AI. "Each comes with a combination of scale, local data depth, and genuine urgency that global models have not been trained for," he told Business Standard.

 

Experts say these sectors stand out because they combine three essential ingredients for successful AI adoption: abundant data, urgent problems and large populations.

 

• Healthcare: shortage of doctors, diagnostic burden, hospital data, medical transcription, radiology, insurance claims
• Agriculture: weather, pest alerts, crop advisory, mandi prices, credit and insurance
• Education: multilingual learning, teacher shortage, personalised tutoring

 

Manish Mohta, founder of data annotation and labelling firm Learning Spiral, said governance and financial services should also be on the priority list because AI can improve public service delivery, strengthen financial inclusion and provide more personalised services to citizens and businesses alike.

 

For Rustom Lawyer, co-founder and CEO of Augnito, a healthcare AI platform, sectors such as manufacturing and logistics also deserve attention because AI can improve efficiency, decision-making and resource utilisation where operational challenges are often complex.

 

Can Indian startups move beyond AI wrappers?

 

Another question that comes up is whether Indian startups are creating genuine innovation or simply building interfaces around global AI models. The answer, experts say, is a mix of both.

 

While many early-stage companies do use global models as their underlying frameworks, the long-term value is being driven by teams that heavily customise these engines.

 

"The strongest innovators are building solutions around uniquely Indian realities—multilingual populations, fragmented infrastructure, diverse regulations, and affordability constraints. Solving these complex local problems demands deep domain expertise, workflow integration, and an understanding of how technology is adopted on the ground," said Lawyer.

 

Why could domain-specific AI win?

 

While global AI models like ChatGPT and Claude offer complete solutions, Indian homegrown AI models can differentiate themselves by building for local languages, public-service use cases, sector-specific needs and India’s vast digital public infrastructure.

 

According to Mohta, businesses need AI that understands their industry, meets sector-specific regulatory requirements, and delivers relevant, timely and accurate insights based on their unique business context.

 

Prakash said the real moat lies in proprietary data loops. "In education, for example, an AI that learns from years of live instruction data across Indian curricula, regional learning styles, and the specific ways children in different contexts get stuck carries knowledge that no English-language training corpus can replicate," he said.

 

Can India become the AI partner for the Global South?

 

According to Mohta, many emerging economies across Asia, Africa and Latin America face challenges similar to India’s, including linguistic diversity, limited resources and the need for affordable technology. This presents an opportunity for Indian firms to establish themselves as reliable providers of practical, scalable and cost-effective AI solutions.

 

Prakash argued that India’s greatest AI advantage may lie in the very constraints it has learned to navigate. From multilingual users and regional pricing to fragmented infrastructure and diverse operating conditions, Indian companies have built products that solve problems common across the Global South. That experience could make India an exporter not just of AI models, but of AI systems designed for real-world complexities.