Sramana Mitra: How many were you able to get to start building out your model?
Oleg Rogynskyy: Graduating from Y Combinator helped us become very successful. We managed to get from Zero to 100 POC customers in the first 90 days. This was when we were in the Y Combinator. We got 100 companies on the platform on the demo day.
Sramana Mitra: When you went into Y Combinator, were you just at a concept stage?
Oleg Rogynskyy: Yes, we had a completely different idea. We not only managed to sell the product, but we also managed to build it.
Sramana Mitra: Okay, so your Y Combinator idea was based on a different idea and not on this idea?
Oleg Rogynskyy: The data platform would be similar, but the use case would be different. We were going to build a system that scores how good a salesperson is based on their activity so that you can use that in hiring.
Sramana Mitra: Interesting. What was the pivot based on? Was it based on customer conversation?
Oleg Rogynskyy: Yes. Customers were saying, “Hey, this kind of sounds cute. You are trying to score my salespeople, but can you just tell me what they are doing in the first place? What can you do to make them better?”
Sramana Mitra: I see. You followed the customer emergent process using that original hypothesis and then pivoted based on the conversations that you had and learned what the customers were looking for and then spun it differently.
Oleg Rogynskyy: Exactly.
Sramana Mitra: In building this company to this point what else have you done that is innovative and original that you think we should discuss?
Oleg Rogynskyy: It’s been almost six years of building interesting and innovative things. After we graduated from YC, we started building, and we initially were focused on anybody who would sign up, but that doesn’t fly as well. We ended up finding our first big customer six months after YC. This was Palo Alto Networks. This showed us how important enterprise compatibility, features, and security is.
We started with Palo Alto Networks and became successful there on the enterprise side. It taught us to train our AI. Going through a larger company is harder because you have to do more work to get in. On the other hand, Palo Alto is more valuable from an AI training perspective than 1,000 small companies. In terms of scaling your AI, working with large companies was the path.
That was a huge insight for us. We have focused on large enterprises ever since. That was great. There is another interesting thing. You need to have as tight of a use case coverage as you can. You cannot just be training AI for something; you have to be solving a specific problem. What was helpful was discovering that methodologies like value selling in sales were helping us decrease the aperture of what we need to train the AI for.
It also showed us what kind of outcomes people are looking for if we align with the methodology. You need absolute focus in refining your focus to more and more precise things that you are trying to do. It’s basically doing fewer things better vs doing many things okay. This was a driver for us.
Lastly, the big invention that made our business defensible besides matching technology was what we call the People Graph. It turns out that understanding who is in this activity and why they are here is the key piece of information that you can have on our business. We started building our graph of persona information to understand things like who the people are, where they have been before, what they are like, what they are focused on, and so on under the hood. This allowed us to have a first-party data platform that tells us all the personal insights which is very unique in the industry.