A Product Market Fit Show | Startup Podcast for Founders
00:18:40 · Wardah Inam
[00:18:40] S0: That that aspect of it I think we just did not like, basically in terms of what margins could be, et cetera. That was one piece of it. It didn’t have major successes that people could point to. There were some companies like WIZ.AI. I think the first generation of companies which could not – and especially healthcare could not make it work at a larger scale. They did solve a very good problem. However, adoption of that technology was not really happening at the scale that visas would like it to work. There was actually a market problem as also there was an AI problem. The adoption was happening at that time. I would say third, this is something that I didn’t want it to be true. I still avoided it to be true, but I do think there was a bias in terms of for me to be a woman starting this company as well. Even if I was hearing it, I was not trying to internalize it, which was where an investor – this was an angel investor. They have a fund, a pre-seed fund, who basically told me that, hey, I really like you. However, the chances of you getting funded next are so low that I don’t want to put money in because I think the statistics they shared with me was only 2% of female founders get funded. If you get a male co-founder, I’ll fund the company, or male CEO. It wasn’t about a co-founder had a male co-founder, but it was getting the male CEO in. I don’t think they were doing it from bad intentions or anything. They actually believed it. That’s one where I think there was a little bit of this pattern recognition that VCs do. I think that that’s one where it was hurting us a little as well. The amount of traction they wanted us to have at that time was much higher than normally I would expect a startup to get funded.
00:20:45 · Pablo Srugo
[00:20:45] S1: Pattern recognition is a funny thing because on one hand, it’s the tool that both species need to rely on in order to understand what things might work, but it’s almost baked into pattern recognition that what you end up forming are biases. Some of them are helpful because you’re like, this thing tends to work, but some of them are actually, they create these crazy blind spots. Obviously in your case when it comes to female founders, there’s so many other examples of spaces, areas where people say, oh, nothing in this area could be big because there’s been nothing that’s big, and all of a sudden, you miss the next one. That’s a pretty wild story and black and white bias there, and going through that. That’s hard to imagine. Take me through then, because at one point you do overcome this. You over time raised over $100 million and just looking here like your seed round in 2020 was pretty big. It was almost $8 million. What happened between the time where you raised from that that you had your savings, you had the angel, you had a few, let’s say $100,000 or so to that $8 million round? What did you accomplish in that time? Because that’s a big seed round.
00:21:46 · Wardah Inam
[00:21:46] S0: The first institutional check that was written by E14 fund, so MIT’s fund, I think they took a chance on us, so it took a chance on me. I think that was very crucial in starting to make it into a real company. Before that, I think of it as more of a project that was happening. That was one thing that happened, as well as we got space in at Harvard Innovation Labs. We had a space and we had an institutional investor behind us, and then it was literally traction. We had to prove that this works. We had to get customers. On one side, we were trying to figure out how do we commercialize this technology, which can have so much impact. We found that insurance companies had the biggest need and urgent need in terms of – they were reviewing a lot of claims manually, and they were making the same decisions that dentists were making, so they had hired a team of dentists who were looking at this data manually and making these decisions. We knew that we could help solve some of the – at least automate all the administrative tasks that they were doing, checking for completeness of a claim, checking for, is this the right x-ray, the data, et cetera? Then we could actually start to look at helping them in clinical aspects of it as well, so medical necessity. We started observing that, trying to figure out what that would look like. At that time, I think from the investor’s belief was insurance is very slow, so you guys are going to die before you get a contract there. Now, I understand it. I now understand how slow they are. But at that time, I think naivety helped us a little because I said, seemed like they’re moving. It seems like there’s a need, so we started actually pursuing that. I would say once we built enough that we could show a demo, we were able to get insurance companies. Then by the time, you mentioned the seed route, we had signed Guardian and Delta Dental. These are large insurance companies.
00:23:52 · Pablo Srugo, Wardah Inam
[00:23:52] S1: Why do you think insurance companies were the better first customers than dentists themselves? [00:23:58] S0: For dentists, we needed FDA clearance, and that itself is a longer process. We started the FDA process while we needed actual customers to get the funding, to build what we needed to build. [00:24:12] S1: Perfect. That makes sense. We’ll stop it there. We’ll end on the two questions that we always end on. The first one is, when did you feel like you had true product market fit?
00:24:12 · Pablo Srugo, Wardah Inam
[00:24:12] S1: Perfect. That makes sense. We’ll stop it there. We’ll end on the two questions that we always end on. The first one is, when did you feel like you had true product market fit? [00:24:22] S0: For me, I started feeling that this was real when we started signing these insurance companies. The way it worked was we actually got these two insurance companies that we piloted in. We showed that it actually works and it could work. After that, there was this inbound from a lot of other insurance companies doing RFPs to see if this works. We basically won every RFP that we participated in. That hadn’t translated into revenue yet, but for us, it was like, okay, this is real. This is going to be huge, and this is going to have a huge impact, and the customers were seeing it. If you actually talk to even a customer right now, they see the same thing that they were seeing at that day. This is magical. People jaws were dropping when they were seeing our demos, and I think it’s harder to – revenue came in later, but I think that earlier, the demos were – when we actually do our demo, I would say, one out of three would get a wow. Then after that, when people would actually deploy it, mostly they would say, oh, the results were much better than we even imagined in the beginning. I think that’s where, for me, it started. [00:25:35] S1: What was the number one RI for these insurance companies? Was it not, let’s say being able to deny some claims, or was it just efficiency and just having fewer people on staff reviewing claims as they came in?
00:25:47 · Wardah Inam
[00:25:47] S0: I the same problem that we were facing or I faced was you show data to two different dentists. They’re going to say different things. How do you know what is right? Now, what you don’t want is a dentist on the other side saying, I don’t think this is what it is. Then they’re going back and forth with each other, and then now bringing in – and these are visualizations that come into play. There’s quantification that we add into this data. Now, two people can at least have the same rulers. They can have the same information, and then they can actually talk to each other and be on the same page. I think that is what really changed in dentistry. I think for these insurance companies as well, that translated into higher consistency in decision-making. Now, the amount of training that they had to do of all these people – and then people could spend time on the real things rather than spending time on things that were super easy. Everybody knows, as I mentioned it as like administrative tasks. Anybody can do that, and getting that out of the mix as well, and making sure that then they’re spending time on the right things. They’re focused on the right problems. Then even for the reviewers who were using this technology, some of it got fully automated, and that was great. These were all the sets, so now you’re making faster decisions, so it’s better for the provider. The ROI was pretty high in terms of financially, but also quality-wise, and provider experience-wise. It was a game-changer in how they were actually operating.