A Product Market Fit Show | Startup Podcast for Founders
00:11:34 · Wardah Inam
[00:11:34] S0: The way we started building this out was trying to understand the problem. We didn’t know that this could be done. We didn’t know what was possible and what was not possible. A lot of it was once we got the data in dental practices, we started just doing data analysis of and saying, okay, what is the prevalence of this disease? What does this look like? Why are people diagnosing a certain way? There was a lot of things that we found. For example, one of the things that’s key to Overjet even now is everybody thinks that there’s more overdiagnosis in dentistry than underdiagnosis. If I chat with most people, they will say, yes, there is an incentive for dentists to produce more. That is why telling you that there are conditions there, there’s the trust level suffers because of it. There is significant amount more underdiagnosis in dentistry and treatments that are not happening than overdiagnosis. That we found by actually analyzing the data and looking at the rates that CDC was saying in terms of prevalence of a certain disease, what we were finding, and how much it was getting diagnosed. I think there was a lot of interesting things that we found just from the data itself. Being in the chair, you can realize that dentist job might be tough. But if you are observing how they’re interacting with not only you, but other patients at the same time that you are in the practice, you start realizing how tough that job really is. Especially coming from the MRI side where radiologists are spending, a lot of time looking at x-rays and other MRI data, and usually in darker rooms, and with the right monitors, et cetera, that’s not what dentists are utilizing. They’re moving rooms. They’re changing lightning conditions. They’re having these old monitors that they’re making diagnosis on. There’s a lot of interesting learnings that I don’t think that the industry knew and had the belief in. But I think as we started to look at it from the data, and as a data problem, we started saying, the data quality is bad. The way things are being monitored is bad. The monitors that they have is bad. It’s not like dentists don’t want to do the right thing. The tools are not there, and we can do something about it.
00:13:54 · Pablo Srugo
[00:13:54] S1: What was that first solution that you conceived of? Because today, my understanding is you just automate that. You look at images and use AI to understand if there’s something wrong or make diagnoses. Was that the idea that you had at that point?
00:14:08 · Wardah Inam
[00:14:08] S0: Yeah. From the beginning, we were looking at extras, and extracting or analyzing disease on extras. The interesting thing about dentistry is that if you went to a dental practice and you wanted to query how many patients have cavities, you won’t be able to query that because disease is not recorded in dental practices. Treatments are recorded, but disease is not recorded. The disease actually sits in x-rays, sets in notes, et cetera. That’s what we started doing was like, okay, let’s start to analyze disease. But what was really unique about us was that we started to quantify disease. Rather than saying, hey, there’s a cavity, we started saying, hey, here’s a cavity which is 25% of your crown area of the tooth. That means you need this treatment. That is surpassing dentist capabilities as well, but also making very objective. Now, you can actually communicate, going back to my original problem, where two dentists are disagreeing with each other. Now, you can actually say, oh, I’m disagreeing because of this, and this person is saying because of Y, because now you can start to quantify disease and start to say, okay, it’s X percent in enamel, it’s indent, and this is what it looks like. I think that helps in much more objective decision-making, but also then extracting that data so you can actually do many interesting things with that information and try to solve this more societal problem as well.
00:15:35 · Pablo Srugo, Wardah Inam
[00:15:35] S1: The other part of the storyline that we’re not talking about here is at this point, you’re a solo founder, you’re a first time founder, and you need to build a pretty sophisticated system. Do you go out and fundraise? What’s your move as the idea starts to form? How do you think about actually executing against it? [00:15:52] S0: First thing was, okay, I need a co-founder dentist. I tried to find somebody who would be my co-founder in starting the company. With that, I started reaching out to many different dentists or talking to them, trying to figure out who would that person be. There was a dentist that I found who was doing his residency at Harvard, common connection we had that who used to be at MIT before and was interested in entrepreneurship. He was there in different entrepreneurship events, so I reached out to him. His name was Alex, and we started working on this project, but he was still in school at that time. The idea was that he would quit school, this was his residency, and work on this full-time, or at least take a leave of absence. But Harvard did not give him a leave of absence to work on the project, so he was with us part-time and helping us provide the dental knowledge that was needed. [00:16:48] S1: Got it. Then what do you do about fundraising? Do you apply to some accelerators? Do you start pitching angel investors?
00:16:48 · Pablo Srugo, Wardah Inam
[00:16:48] S1: Got it. Then what do you do about fundraising? Do you apply to some accelerators? Do you start pitching angel investors? [00:16:54] S0: I had worked for a year. That means I had some savings. At that time, I thought that was a lot of money because that was my first job. I basically start utilizing the savings I had. That’s when we hire Deepak as well, who worked with me at Q bio. Before, he was at Amazon. Also, my parents had given me – when I had graduated and married, they had given me a check for putting down payment on our house. We used that. That was our seed money there. Then I started talking to dentist there, and then there was another dentist who provided the person who gave us the initial data. He provided the second check, and that lasted us for about a year. [00:17:37] S1: Okay. In that year, where did you think about where you had to get the company to in order to raise that next round? [00:17:46] S0: If I go back, now I can – I think sometimes you tell the story better, looking back. But I think at that time, we knew that we needed some customer to get the funding. We started pitching to investors as well. It wasn’t the easiest thing to get funding. I did not have a network of investors. Now, I know a lot of investors. At that time, I knew nobody. [00:18:09] S1: What were some of the responses that you were getting from the ones that did take the meeting and, let’s say passed? What was some of the feedback you got back then?
00:18:09 · Pablo Srugo, Wardah Inam
[00:18:09] S1: What were some of the responses that you were getting from the ones that did take the meeting and, let’s say passed? What was some of the feedback you got back then? [00:18:17] S0: I think right now, people think AI is the hottest topic, but I don’t think it was at that time. From my side, it was very obvious that this is what we should be working on. However, I think there was, because inference has a cost, the way software traditionally has not had a cost – [00:18:37] S1: There’s no unit cost. The marginal cost is zero for software. That’s right, yeah.