Indian AI Startups Shift from App-Layer Tools to Building Core Foundation Models in Science, Engineering, and Neuroscience

Indian AI startups are pivoting from consumer-facing apps to developing core foundation models targeting specialized fields like science, engineering, and neuroscience.

This strategic move reflects a maturing ecosystem aiming for greater independence from foreign AI giants and customized solutions for local needs.

Why Indian Startups Are Building Core Models

Historically, many Indian AI ventures started with app-layer products built atop models like GPT or Llama, but rising costs and data privacy concerns prompted the shift.

Sarvam AI, a Bengaluru-based leader, is now focusing on Indic language foundation models to serve India's diverse linguistic landscape.

Other players like Fractal Analytics and Tech Mahindra are developing sector-specific models under government-backed IndiaAI initiatives.

The transition leverages open-source tools such as Ollama and vLLM for efficient local inference, reducing reliance on cloud APIs.

Impacts on India's AI Landscape

This shift enhances data sovereignty, enabling startups to train models on local datasets without international dependencies.

Business impacts include lower operational costs through small language models (SLMs) and improved performance for enterprise applications.

Experts predict this will spur innovation in neuroscience and engineering, positioning India as a global AI powerhouse.

Looking ahead, government support via Phase 2 of IndiaAI, funding 12 foundation model teams, signals robust future growth.

Challenges like compute shortages persist, but investments in domestic GPUs promise to overcome them.