pi Ventures looks to invest 18-12 startups in the healthcare, logistics, retail, fintech and enterprise sectors using AI, Machine Learning and IoT
The Bangalore-based VC firm, which looks to invest in Applied Artificial Intelligence (AI), Machine Learning (ML) and IoT-focused startups, is looking to make the final close at US$30 million within this year.
SIDBI, a leading lender to small businesses in India, besides prominent family offices from the US, Canada, Singapore and India, have backed the fund. Other Limited Partners include Mohandas Pai (ex-CFO of Infosys), Binny Bansal (Flipkart Co-founder), Deep Kalra (MakeMyTrip Co-founder), Sanjeev Bikchandani (InfoEdge) and Bhupen Shah.
Founded in 2016 by Manish Singhal (Co-founder of LetsVenture) and Umakant Soni, pi Ventures invests in early-stage startups that focus on solving problems in healthcare, logistics, retail, fintech and enterprise sectors using AI, ML and IoT. The VC firm to invest in 18-20 startups from this fund over a period of three to four years.
Singhal said: “India is the dark horse in the AI race given it has lot of data, brilliant data science talent and early adoption environment due to broken processes and we are happy to play a part in helping India leapfrog with AI. We are thankful to our early backers, investors and founders who have chosen to work with us.”
pi Ventures has made three investments so far in the healthcare and energy-efficiency space, namely SigTuple, Zenatix and ten3T.
SigTuple is in the medical diagnostic space and is creating a deep learning-driven, cloud-based solution for detection of abnormalities and trends in medical data, for blood diagnosis. Zenatix is a data-driven energy efficiency company that uses advanced machine learning based models and delivers 10 per cent to 30 per cent energy efficiency to large commercial consumers of electricity along with predictive insights to prevent downtown.
ten3T is in continuous monitoring of medical data like ECG in real time through its own smart patches to enable mobile ICU for smaller hospitals.
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