Bootstrapping an Artificial Intelligence Startup with Services: Nitesh Chawla, Founder, Aunalytics (Part 3)

Sramana Mitra: How much seed funding did you get?

Nitesh Chawla: I don’t remember, but it was enough to pay salaries for multiple individuals.

Sramana Mitra: Like in the hundreds of thousands?

Nitesh Chawla: Hundreds of thousands. David Cieslak joined who’s now the Chief Data Scientist. He’s my first PhD student. He got his undergrad, Master’s, and PhD at Notre Dame. A few other of my undergrads joined. One was an award-winning undergrad. He was one of the best programmers I have known. We huddled to start building the first iteration of Aunsight. Aunsight is a flagship platform product. In that first year, we validated some of the ideas that we thought would be game-changing for us.

Sramana Mitra: What does validation mean in that?

Nitesh Chawla: What we were clear about going after is the mid-market. If you think about large banks, they have centers of machine learning. They have millions of investments in some cloud or database companies. Then there are these small to midsized banks. Every city has a few. As well as in the mid-market, there’s manufacturing, media, or retail. They’re not quite there yet at that time.

The idea was, let’s work with them almost in a consultative capacity while building up the product. Instead of taking the more typical Silicon Valley style, I wanted it to be more contemplative to ensure that we don’t build something for which there is no consumer. We wanted to build what we believed would be used. We had certain principles that it should be database agnostic.

I’m not going to fight with anyone who wants to build a data warehouse or datalake. You could have whatever data warehouse; you still need to get the right dataset. My goal was to go after the small data, do a really good job building that, and let everyone fight around the big data.

Sramana Mitra: I just want to clarify here. You’re talking about the data warehouse versus the data on which you’re building your models.

Nitesh Chawla: Yes.

Sramana Mitra: The industry defines big data as data that requires machine learning to process. What you’re calling big data is data warehouse. That’s not necessarily what we call big data in industry. I think you are doing big data and machine learning, but you are doing it in the context of solving a problem as opposed to the huge universe of data that is relevant to that.

Nitesh Chawla: Exactly. Today when we look at big data, it is something that we are using machine learning on.

Sramana Mitra: I have been covering big data on my blog for a long time. We have always used this definition of data that requires machine learning.

Nitesh Chawla: I really like that definition. Better than what it was with the four V’s.

This segment is part 3 in the series : Bootstrapping an Artificial Intelligence Startup with Services: Nitesh Chawla, Founder, Aunalytics
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