Start-ups have opportunity to leverage data, AI for growth: Manish Singhal

Manish Singhal, Founding Partner at Pi Ventures
As India has become a centre for data science for several global firms, start-ups are looking at the opportunity to leverage local data to build solutions for India's problems. As more entrepreneurs emerge, investors are not far behind. "We meet almost five to six Artificial Intelligence product companies a week," said Manish Singhal, Founding Partner at Pi Ventures, in an interview with Alnoor Peermohamed.

How did you come up with the idea to start a venture capital fund only to invest in Artificial Intelligence and Machine Learning companies?

So I invested in this company in 2015 called Locus. They apply machine learning to logistics and that got me really thinking about the power of data and what you can do with it. When that investment happened there was this penny drop moment of "Why are we not looking at Machine Learning and Artificial Intelligence as a space to invest in?" So we did a lot of ground work, almost five to six months and then nearly a year back we launched Pi Ventures. We're a $30 million fund which exclusively invests in applied cases of ML and AI.

We realised that data is becoming an integral part of everything. Products in the future would be those that are able to make some intelligent decision based on the data, not based on rules which was the previous thesis.

How many companies have you invested in so far?

We have invested in three companies and within this month our fourth investment might also come through. We are at advanced stages of talks, the term sheet and other things have been given and the due diligence is almost done.

AI and ML is a very broad sector, but are you investing in product companies or ones looking at services?

Product companies I would say. We are not funding technology for the sake of technology, we are very rooted in a real business case being there. Two of our companies are in healthcare, one does blood pathology using computer vision, they are filing patents for what they've built and this built out of India. Anand Diagnostics is already signing them on and they're planning to go commercial in three months, that's how close they are.

People say there are not too many product companies in India that are using AI in a true fashion. Is that true?

We meet almost five-six AI product companies every week. Recently Zinnov published a report according to which India is the third largest AI in the world and there are three or four factors for this. One is India has data talent. These big global companies had set up their data science teams in India and people are now coming out from there and setting up start-ups. Data in India is cheap and available and that makes sense to consider when you see that for a medical company data is five to six times more expensive to buy in the US than compared to India. The third is, the businesses in India are willing to buy from Indian start-ups and that's a huge change. The fourth factor is not about India alone, but the Internet and computational power has dramatically increased over the years.

You have raised about Rs 200 crore to invest in AI companies. How fast will you look at deploying that?

Our investment period will be three to four years and we will look at investing in 18-20 companies. We will do fewer investments, but we will go more in depth. We'd like to get in early, pre-series A and our round sizes will be between $300,000 to $400,000, but in some cases we may go higher or lower and we will follow on as well. We have sufficient capital to participate in follow-on rounds of funding.

How have you trained yourselves to separate the fad from the true innovation in AI?

The standard things that you look for like a good team, large enough market size are still relevant, but we look for three additional things. We look at IP very strongly and when I say IP I don't just mean patents, but the kind of algorithms a company has developed and how defensible they are. The second thing we see is data acquisition, because data is at the heart of an AI engine. How will a company get it, use it, and once they scale how much it is going to cost to buy. So that data strategy is something we go deep into. The third factor we look for is how deep a company is rooted in business values. The company in which we have invested that I used as an example earlier, they are going to generate revenues very shortly, so we look for that.

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