Bootstrapping a Two-Sided Marketplace: Mort Fertel, CEO of SudShare (Part 1)

This is an insightful story of a family that has built an interesting two-sided marketplace business for outsourcing laundry services.

Sramana Mitra: Let’s start at the very beginning of your journey. Where are you from? Where were you born and raised? In what kind of background?

548th 1Mby1M Entrepreneurship Podcast with Lloyed Lobo, Co-Founder of Boast.ai (Part 6)

Sramana Mitra: ARR is now your business model right?

Lloyed Lobo: We look at it as ARR. For every year a company does R&D, they pay us. It’s recurring. Although we take a percentage, it’s very consistent with how much R&D they do. That’s how we model it out. 

Sramana Mitra: How many customers is $10 million ARR for you?

548th 1Mby1M Entrepreneurship Podcast with Lloyed Lobo, Co-Founder of Boast.ai (Part 5)

Sramana Mitra: How did you manage the early phase where you had to stay afloat to get paid?

Lloyed Lobo: With the help of loans, houses bank loans, credit cards, and very lean teams.

Sramana Mitra: How many were you?

Lloyed Lobo: When we started, it was just me and my co-founder. Then there was a whole bunch of people. It was hard making payroll. A lot of credit to my co-founder because he funded it out of his personal cash to make payroll many times. We also got loans on my house and Alex’s house. They were very hard times. One of the things for us was our wives have been very supportive.

Thought Leaders in Artificial Intelligence: Salil and Kiran Pande, Co-Founders of VMock (Part 1)

Salil and Kiran Pande have built a capital-efficient AI company from India addressing the global career services market.

It’s a wonderful conversation that works in both AI thought leadership and also their entrepreneurial journey. Enjoy!

Sramana Mitra: Let’s start by introducing our audience to yourself, your co-founder, and VMock.

548th 1Mby1M Entrepreneurship Podcast with Lloyed Lobo, Co-Founder of Boast.ai (Part 4)

Sramana Mitra: Let me probe one thing here. When you were doing this manual customer servicing, was it more of a rule-based engine than pure AI?

Lloyed Lobo: First, it was fully manual. The rules-based engine was the Wizard-of-Oz MVP. It looks like technology but, on the backend, humans are doing it. There are two elements. You are collecting data. Then you need to process that data and generate the outcome. Then we started doing this data collection online where people can upload everything.

Bootstrapping Using Services, then Raising $10M and Scaling to $16M: EZOPS Co-Founders Sarva Srinivasan and Dutt Chintalapati (Part 5)

Sramana Mitra: In 2018, are you still self-financed?

Sarva Srinivasan: Yes. At the end of 2018, we did a Series A. 

Sramana Mitra: How much did you raise?

Sarva Srinivasan: $10 million.

548th 1Mby1M Entrepreneurship Podcast with Lloyed Lobo, Co-Founder of Boast.ai (Part 3)

Sramana Mitra: How about getting your first product out? Were you bootstrapping to get to the first product?

Lloyed Lobo: We bootstrapped to about $10 million ARR. We just raised a round of funding around Thanksgiving last year.

Sramana Mitra: Let’s talk through the early part. You bootstrapped and you had validation because of your consulting work. How much of that consulting pool became customers of the product?

Bootstrapping Using Services, then Raising $10M and Scaling to $16M: EZOPS Co-Founders Sarva Srinivasan and Dutt Chintalapati (Part 4)

Sramana Mitra: You have a wonderful positioning because anything that has a compliance angle to it has a very mission-critical purchase cycle. You must have experienced pretty high-velocity customer building.

Sarva Srinivasan: That’s correct. In parallel, we started building EZOPS. As part of our engagement, we were talking with the clients and their challenges. Oftentimes, it was just a lack of an end-to-end platform. It could be that I have the wrong data. Therefore, I’m not able to send the right information to the regulators. It could be that I have 10 different applications sending the same data and I can’t figure out what the right data is. These are very simple problems.