Introduction to Guest

John-Isaac Clark, known as “JC”, is the CEO of Arturo, an AI property analytics company that helps Insurance Carriers improve the accuracy and speed of decision making across claims, underwriting, pricing, and renewals. JC possesses more than 10 years in geographical and location-based analytics with a start-up background and experience. Formerly, JC was the head of Commercial Product at DigitalGlobe and considers himself a recovering software engineer.

JC’s journey started at a very young age as a self-taught programmer. Since JC was eight years old, he was always fascinated by computers and technology, especially since his father was in the industry, before there really was an industry. When JC was only 19, he wound up joining a startup in Chicago, leaving university behind to become an entrepreneur. The startup had some success, which led him to work in San Francisco and Austin for a few years. JC also worked through the .com era, which was an experience within itself. This time in JC’s life gave him a lot of exposure to being an entrepreneur and the startup culture in general. He started to ask questions like “Why are we building this?” and “What is the value we are creating with this?”. Through his experiences, JC found his spark as a Product Manager. Overtime, through one of JC’s last startups, T- Sciences , he worked closely with Google Earth and Google Maps, gravitating towards the product side rather than the software engineering side (JC still considers himself a recovering software engineer, working on little projects here and there). This is just a snippet of how JC went from the software engineering side to CEO of an AI company.

Coming Up with The Idea

In 2006, JC and his brother co-founded a company called T-Sciences where they both had the opportunity to work very closely with Google Earth, investing heavily in mapping. Through this partnership, T- Sciences had a picture of the world in 3D something that no one has had before. This was the time when Google Maps, directions and Street View (to name a few) blew up in 2006. Resulting from this new, powerful technology, everything from Amazon deliveries, DoorDash, Uber, and so many things we use in our personal lives were in the hands of consumers. However, there wasn’t a way that this geospatial location-based information had fundamentally changed the enterprise businesses operated.

When the business at T- Sciences was in the process of exiting, JC made the decision to join Digital Global, which at the time was still the world’s largest satellite imaging company. The product that JC was leading had a product for a platform that was applying AI and machine learning and big data analytics to 175 petabytes of imagery going back 17 years over the surface of the planet. This is obviously a lot of the content that you see in Google Earth, Google Maps, Apple Maps, etc. The goal, however, was not to show people the images, but instead: ‘How do we get information out of these images? How do we use machine learning? How can you extract interesting things like where roads are, where populations are?’ To JC, these questions were the step in the right direction, the step towards having something transformative for an enterprise. This was more of a platform style play where people would come in and write their own algorithms or create their own AI models and then run it in this environment on imagery.

Interestingly enough, one of the companies using JC’s product was a fortune 500 company called American Family Insurance. After using the product, American Family Insurance asked JC to meet with them, American Family Insurance had used the product JC created on the satellite imaging side to take aerial imagery and ground level imagery of all of their residential properties. They created the capability to fetch all of these images and run them through deep learning models to develop structured data, similar to what you would get if you sent an inspector out to the property about how many stories it is, the perimeter of the building, what type of roof it is, the material, condition, etc. This was done all through machine learning from the latest images available in the property. JC was beyond thrilled and excited. Here was a solution to do what he had been passionately longing to do for a long time, at least since he began working hand in hand with Google.

Testing the Product & Validating the Market

American Family had created a unique set of technology that they wanted to expand to other insurers around the world. At the end of the day, however, they are a property analytics company that’s applied to: insurance, the residential property, and casualty insurance market. If JC is going to value the property, not insure it, many of those things that make it insurable also contribute to its value. For example: condition, number of story sizes, etc. can help determine how much it’s really worth in the market. With that being said, American Family had new technology spinning out, which led to the founding of Arturo.

Over a period of about seven months, Arturo went to market and listened to the customers, engaging with companies like Hippo Insurance and their longest standing customers. While the IP agreement and negotiation was being figured out by big lawyers, Arturo was really understanding how to take a product that sat inside American Family and use it as more of a research capability that could work across a number of different companies. Ultimately, Arturo got spun out from American Family where they now serve customers both in the US and internationally.

Using their new technology, Arturo’s focus has been: ‘How do we price a property really accurately while really quickly providing a fantastic experience for their customers?’ The first part was to take the technology that was designed to serve one customer at American Family and make it scalable so Arturo could serve dozens or hundreds of customers in time, which came more from the software engineering side, making the platform and the infrastructure more robust to handle more inbound API requests.

Arturo also recently announced that they processed, with their client Suncorp, nearly 9 million properties – every residential property in Australia – within two days (48 hours). One year ago, this would have taken almost a year and a half to build out a data set similar, whereas now it is accomplished in two days.

Technology advances because of the effort put into Arturo’s engineering team. They are also figuring out from a product market fit perspective where the ability lies to suckin all images of a property while understanding what the property condition is. Arturo is focused on really discovering how to use this technology to scale from a software engineering perspective to serve all customers at once.

Challenges Encountered

There are so many perceptions that AI and Machine Learning are going to replace humans, which creates a fear amongst the masses that people are going to lose their jobs to robots. This, however, is not true. In fact, JC finds this ironic because at the end of the day, building startups are all about people. They’re about the importance of people and the relationships that they have, whether those people are your customers or your teammates. And no matter what you’re using, Machine Learning or AI, it’s still always about the people. A challenge is teaching people and companies the value of Artificial Intelligence.

As a first time CEO, JC has made mistakes, same with the members of his team. But the growth lies in how you come together as a team in each and every moment. For example, managing COVID has been a massive challenge, yet having a sense of togetherness has helped the team thrive. This is one of the biggest challenges JC has encountered. Hiring people during COVID where no one has ever been in the same room or met face to face. This can be challenging. However, as stated, it’s about building relationships with customers and team members while communicating the value of the product. It starts by recognizing and acknowledging the challenges in order to put time and effort behind them. JC values a great work environment and customer success team, one that is fully present to work with customers while making sure they’re successful using Arturo’s technology.

One of the things that was realized early was the ability for a customer to trust the outputs from machine learning, to know when they should trust them and when they shouldn’t trust machine learning. To JC, this is really important. Confidence framework was something Arturo invested heavily in.

Future Endeavors

Arturo’s future endeavors include growing beyond just insurance. The better the company understands physical properties in the world, the more other industries can benefit from their product. Now, Arturo has the ability to look at a home or property and identify any issues, this is before someone comes in to give a full inspection. This ability a huge use case Arturo sees itself moving into in the future. As Arturo receives its series B of funding, they’re able to address these markets more effectively. JC also thinks drones will be in our near future, collecting information about properties.

Luckily, Arturo is having fantastic success, keeping busy with customer demand and interest. With a focus on customer success, not just revenue, Arturo makes sure each customer gets exactly what they paid for, a continual focus into the future.

https://www.arturo.ai/