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Bootstrapping a Virtual Company to Scale: Taso Du Val, CEO of Toptal (Part 7)

Sramana Mitra: Have you bootstrapped your company all the way or have you raised money along the way?

Taso Du Val: We raised a really small round for approximately $1.4 million with some really great individuals. It was a seed round, so it was not a priced round. We still consider ourselves a bootstrapped startup. Hence, we only did everything off of convertible notes. That was about two and a half years into starting the company. It was a very different experience relative to a lot of other companies. We’ve only taken a seed round.

Sramana Mitra: Tell me what the philosophy is behind that decision. That’s definitely a contrarian philosophy. We are very supportive of bootstrapped entrepreneurs. We’ve probably been one of the biggest supporters of that philosophy. Tell me more about what is your thinking. >>>

Bootstrapping a Virtual Company to Scale: Taso Du Val, CEO of Toptal (Part 5)

Sramana Mitra: I’ll just benchmark it by saying oDesk, for instance, charges 10% of the contract value. Tell me what the additional value is that you’re adding to the process.

Taso Du Val: We screen all of the individuals for you. Not only for our network but also for your specific request. There are many layers of screening. There are many individuals involved in that screening process. There’s very little automation in our screening processes. We’re able to not only look at our network and screen everyone so that they can get in, but we’re also finding the perfect individual and contracting them out to you in an on-demand style.

We only give you one or two people when you post a job. It’s almost always the right person every single time. Our average job post to client ratio is 1.7. That means for every 1.7 person that we give you, you’re going to accept them. That’s an incredible metric that no one in the world can match. World renowned staffing companies  probably have a ratio of eight to one. People don’t even believe it, but that’s actually what we do. >>>

Building an AI-Driven Sales Optimization Company: Vincent Yang, CEO of EverString (Part 6)

Sramana Mitra: What else is interesting strategically in how you’ve navigated the story so far? How many customers do you have now?

Vincent Yang: I think we have four or five dozen customers. The growth rate is actually pretty fast.

Sramana Mitra: What’s the pricing model? What’s the business model?

Vincent Yang: The core of EverString is to provide the predictive model. For every client, if they have multiple products, there will be multiple models running at the same time. For every single model, there is a predictive platform fee. It depends on the scale of the business. For some clients, we charge $30,000 to $100,000. >>>

Bootstrapping a Virtual Company to Scale: Taso Du Val, CEO of Toptal (Part 4)

Sramana Mitra: What is the business model? Is it a commission-based business model? How do you charge?

Taso Du Val: We take a percent of every single contract.

Sramana Mitra: Are you still based in Budapest?

Taso Du Val: No, I’m mostly based in New York and Moscow at the moment. That’s where I’m spending the majority of my time. >>>

Bootstrapping a Virtual Company to Scale: Taso Du Val, CEO of Toptal (Part 3)

Sramana Mitra: Let’s take that on a more granular basis. What year are we talking when you decided, two years in, to give up?

Taso Du Val: That was probably around 2012. In 2012, we said, “Maybe we should figure something else out.” That was two years after we started the company.

Sramana Mitra: Who’s we? Who else was in the project?

Taso Du Val: My co-founder, Breanden Beneschott. After I started it in 2010, I ended up teaming up with my co-founder. He was still at Princeton University studying Chemical Engineering. Funny enough, he was actually one of the clients of Toptal. >>>

Building an AI-Driven Sales Optimization Company: Vincent Yang, CEO of EverString (Part 5)

Vincent Yang: We use, what we call, topic modelling to analyze every single news. For example, is this company acquiring the other company? Is this news about conference sponsorship? How do we know how strong an implication is? We have no idea. That’s the point where we have to rely on machine learning. Machine learning will be very helpful for us in figuring out the correlation of every single business indicator to the final outcome. We analyze tens of thousands of features and also millions of combination of features to find the correlation factors of all of those.

Sramana Mitra: Where did you find your early traction? Was it in the technology industry? >>>

Bootstrapping a Virtual Company to Scale: Taso Du Val, CEO of Toptal (Part 2)

Sramana Mitra: You’re more of a freelance engineering talent exchange?

Taso Du Val: That’s accurate. We think of ourselves as Uber for engineers. It’s funny. A lot of companies have pitched this.

Sramana Mitra: Let’s go from there. At 25, this is what you decided you wanted to do. How did you get the business going?

Taso Du Val: I started to do software consulting with a few individuals, and then contracting out engineers to them that were already working with me. I had individuals in Russia, Argentina, and other places who I was working with for my startup. >>>

Building an AI-Driven Sales Optimization Company: Vincent Yang, CEO of EverString (Part 4)

Sramana Mitra: I’m going to start asking you very deep questions because I did a startup in the sales lead generation area back in 1997. It used NLP and the whole AI stream. It was a bit early. It was well before the Internet had completely established itself. I know a lot about this area in general. Tell me more specifically about exactly what you do by applying your technology to the sales lead generation.

Vincent Yang: We have two use cases. First, we come into any company. We say, “Let us link to your internal work flow data whether it’s CRM or marketing automation data because we want to learn who, historically, are your good leads and who are the bad leads.” We do this for labeling. Then we start using our crawling engines to crawl every single information about those leads.

The underlying assumption is there must be something in common about those good leads. Why are they all converting? Why do those leads that you interact with never convert? We wanted to use a mathematical formula to describe it. This is what we call audience selection. >>>