A clear transition is underway in India’s AI ecosystem as startups move beyond basic applications and begin building advanced technologies in science and engineering. The focus is now on solving complex real-world problems using AI.
Several startups are leading this shift. ZenetiQ is developing a scientific large language model, while HumanTronik is building a personalised LLM designed to mimic human brain functions for enterprise use. Oru’el is working on predicting GPU failures using physics-based models.
At the same time, companies are moving higher up the technology stack. Sarvam has gained attention after launching AI models across vision, language, and voice, and is reportedly raising $300 million at a $1.5 billion valuation. Murf AI has launched its text-to-speech foundational model, Falcon, and is developing a speech-to-text model. Maya Research is also building a speech-focused foundational AI model from scratch.
This marks a major change from last year, when most activity was focused on AI applications and wrappers. Now, innovation is becoming the core focus. “The past couple of years were about understanding what could be done with AI, while now it is all about adding real value, with innovation kicking in,” said Priyanshu Ghosh of Oru’el.
Startups are increasingly working on frontier technologies. Oru’el uses physics-informed AI models trained on real GPU data. ZenetiQ is combining LLM reasoning with predictive capabilities for engineering use cases. “The key idea behind the scientific foundation model we are building is this: can we bridge the gap by fusing the reasoning of LLMs with predictive capability?” said Sashikumaar Ganesan.
HumanTronik is focusing on hyper-personalised AI. “The answer I got was that human creativity is going to be scarce and more important,” said Monish Darda.
Investors are also showing interest in specialised foundation models across sectors like healthcare, materials, and physics.
However, challenges remain. While compute resources have improved, talent is still limited. “The real scarcity isn’t in understanding Transformer theory, but in the engineering required for the parallel training of these models,” said Ganesan. Startups also face issues like lack of testing environments and the need to build trust with clients.
Despite progress, experts believe India still has a long way to go compared to global leaders like the US and China.
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