The Exchanges

Q used the product themselves, they have used many products that under the hood are actually powered by what you built. And so indirectly, they're actually like using stuff built on top of Variance, like a lot of like software infrastructure kind of products. Can you maybe tell people about a Like a specific product that they've used that's, or have probably used, that's powered by Variance and how it works?

A One of the customers, GoFundMe, is a platform that you can use in order to build your own fundraisers. And GoFundMe is effectively a payments platform. And GoFundMe actually has some very strict compliance requirements because they are liable for facilitating payments to, let's say, an organization that they shouldn't have done so. So Variance is used to verify, for instance, if a fundraiser is going to be built for A military operation, for instance, or if you're building a fundraiser for, um, a crisis that you were not part of, which would be fraudulent, well, GoFundMe sort of has the responsibility to be able to detect this. They're using AI agents, so variance AI agents, to conduct those investigations, so make sure that the money is going to the person that they say they are, and also make sure that the money is not going to be funneled to sanctioned countries, for instance, Or be going to, um, anything that could be a compliance risk for them.

AI assessment note: “One of the customers, GoFundMe, is a platform that you can use”

Q What are other like products that people use that also are powered by variants and they don't even realize it?

A So the scale has been, um, impressive. The number of use cases that our agents can be used for. So We have been running complex identity reviews for marketplaces, gig economy platforms, so when you sign up to actually be, for instance, a delivery driver, your identity needs to be verified based on selfies, based on your driver's license. All of that data is then going to be reasoned on by our AI agents, and then it's also going to be validated based on the company's standard operating procedures. That's a good example of how Variance is used. We're also used for complex What we call KYB verifications. So if you sign up to do any sort of business online, whether it's for a marketplace or also to, with a financial institution, they have the compliance requirements to verify that you are actually linked to the business you say you, you own. So a good example is, for example, I sign up to say I'm going to be doing business with variants. Well, The legal name of my company is Decoy Technologies, and it is tied to Korean Malata. That's a simple example, but building that graph at scale is really hard, and oftentimes you're going to see company have multiple shell companies, be tied to multiple different agents and different other identities, and within that really large graph, you expand the area of risk for the company where one of these nodes could be in a sanctioned country. One o…

AI assessment note: “when you sign up to actually be, for instance, a delivery driver”

Q Unstructured, isn't it? Don't you just, like, pull it, like, right, do you pull, like, out of their, like, relational database?

A I, I wish it was this, this simple, but usually, I mean, I'll give you a very concrete example. So, for instance, if you need to verify a fundraiser, I'll pick the fundraiser example, usually the data is going to be scattered around the user identity data, so you need to have information on the user, you need to have information on All of their login behaviors, the devices that they've had, the PII that they've onboarded at the beginning. You need to also be able to pull in information about the business, and then you need to have also all of the information on the fundraiser itself and all of the history of that fundraiser. What has been hard is that oftentimes, whether you're a financial institution or you're a marketplace, that data is going to be scattered across five to 10 different systems. It's going to be into different data stores. And one thing that's been really interesting in that is that sometimes that data is going to be hidden behind a UI. So the only way that the Variance AI agents are able to sort of scoop up that data and reason over it is to be able to directly scrape from a UI that was built for a human. So the data piece and being able to scoop up all that data and bringing it to Variance was one of the hardest technical challenges, really an onboarding one.

AI assessment note: “I wish it was this simple, but usually”

Q Well, that's awesome. Maybe let's change gears here and talk a little bit about the, the origin story. How did you and Michael end up starting this company? How did you end up working on this problem?

A So Michael and I both met, we were co-workers at Apple. Um, we were both engineers on the fraud engineering team, and I was a data engineer. Michael was a machine learning engineer, and it was really interesting because the team in and of itself was sort of the Fraud engineering as a service team of Apple. We were providing our services to the iMessage team, the iCloud team, and we were this centralized fraud team, and Michael's machine learning decisions were then dispatched to the rest of the organization through my own streaming jobs. So we had a very sort of symbiotic relationship, uh, from the get-go. We knew that we worked really well together. I remember telling Michael, oh, well, What if the right vehicle for this product was a company? And I told Michael, oh, we should apply to my Combinator. That was sort of the origin story. I think it really started from the product. We really, really wanted to just see this product exist. Um, and we wanted to see that problem that we were solving at Apple be solved in a much more efficient way, in a much more self-healing and resilient way.

AI assessment note: “Michael and I both met, we were co-workers at Apple.”

Q Okay, so the, the, the first big battle was, like, getting the first customer. Took eight months to land IAC. You really did it the hard way because you went enterprise from the, from the very beginning. Have there been any other hard, like, challenges of building this company?

A I think starting a company tests you in a lot of different ways, um, and we have a really interesting story. You know, after IAC, we got to onboard a lot of great customers in the trust and safety space, um, Medium, of course, GoFundMe, Redbubble, And around July, 20, 24 was one of the times where the company was growing rapidly. We were onboarding more and more enterprise use cases. I think during that month, our revenue was doubling within the month and then doubling the month after. It was really exciting, very step function because it's enterprises. And we had just wrapped up one of the largest trust and safety conferences, uh, called TrustCon in, in San Francisco. And I think a couple, I want to say the day after we had worked so hard for this conference, 12 hours a day, we were super tired. Um, I was going back to the office on a Sunday afternoon, um, and I was on the bike lane, and a truck hit me. That was a really, really crazy experience to, to go through as a founder. I mean, the company was doing well, but at the end of the day, we were A 10 people team, and the CEO just gets hit by a truck.

AI assessment note: “I was on the bike lane, and a truck hit me.”

Q Do you have an example of something like that with one of your customers where the system was able to do something that just like no human team of fraud analysts could ever have been able to do?

A Yeah, one customer specifically, uh, and I think that fraud pattern came to be during the elections. Um, we had one customer that is processing a lot of content, and they're also, they're a fortune 500, they're hosting large communities, and they're also fairly politically exposed. And throughout the elections, because our AI agents had access to the context of entities in relation to other entities, so How does this user fit into all of the other users that we're looking at? We were able to detect really complex fraud rings of especially state sponsored actors that were pushing one narrative over, and I don't think this would have been possible if you had one classifier in isolation that was looking at one piece of content after the other. But because AI agents are able to directly query our data stores, they're able to materialize features on the fly, and they're also able to Use one step to reason over what should be the next step and the next tool call that they make. We were able to detect much more sophisticated fraud rings than you would have been able to do before.

AI assessment note: “Yeah, one customer specifically, uh, and I think that fraud pattern came to be”