Valentine's Day 2025 brought an unexpected gift to the Indian AI ecosystem: GreenAI's Rs 300 crore Series B, the largest funding round for a climate-tech AI company in Indian history. The deal signals growing investor interest in the intersection of AI and sustainability — a space that has attracted significant capital globally but has been slower to develop in India.
GreenAI Series B — Rs 300 Crore
Bangalore-based GreenAI closed a Rs 300 crore Series B to deploy AI-powered energy optimisation across Indian industrial facilities. The company's technology uses machine learning to analyse energy consumption patterns in factories, data centres, and commercial buildings, identifying opportunities for efficiency improvements that human engineers typically miss. The core product is an AI system that connects to a facility's energy management infrastructure and continuously optimises energy consumption in real time, making thousands of small adjustments per day that collectively reduce energy consumption by 20 to 35 percent.
The economic case for GreenAI's technology is compelling. Industrial energy costs in India have risen significantly over the past five years, and the pressure to reduce carbon emissions is increasing from both regulatory requirements and customer expectations. The Series B funding will be used to expand from the 150 facilities currently using GreenAI's technology to 1,000 facilities by the end of 2026. The company is also developing new products for the renewable energy sector — AI systems that optimise the output of solar and wind installations.
The Climate-Tech AI Opportunity
GreenAI's funding round is part of a broader trend of increasing investment in climate-tech AI in India. The country's ambitious renewable energy targets — 500 gigawatts of renewable capacity by 2030 — and its growing industrial sector create a large and urgent market for AI tools that can help achieve these goals more efficiently. The intersection of AI and climate technology is particularly promising because the problems are well-suited to AI approaches: large amounts of sensor data, complex optimisation problems, and clear metrics for success.