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The problem we were trying to solve was not just how we create AI models, but how we operationalized those and use those models to drive the results of our business. My name is Lucas Ward. I'm the co-founder and Chief Technology Officer at Ken Insurance. We are a direct-to-consumer property insurance company. Ask our sales reps to spend as much as 20 minutes per customer verifying a lot of the data attributes. With the Automation Hero platform, we'd be able to create another team that's been able to solve these problems in three minutes per customer, getting much more accurate pricing to the customer and to our sales rep and improving our customer experience. One of the most important goals for Tim, when we're working with customers in these challenging markets, is to be able to provide a great customer experience despite all of the additional processes and data that we need to verify. Automation hearing has allowed us to be able to get those results back and verify the data and provide quick communication to our customers very quickly without having.

Additional people in place are fundamental to having great unit economics which has always been our goal of Ken. One of the improvements we've had is our ability to not only automate certain tasks but able to iterate on that automation quickly based on feedback. It's tempting to think that you can simply figure out every process and just automate it and it will work. But the reality is that no process survives first contact with the enemy and once you start actually having those real-world business cases, you need to be able to iterate on that quickly. And I think that's one of the biggest benefits we've had from Automation Hero as we rolled it out. I think one of the key things that allowed us to have confidence moving forward with automation here was that we were able to use the human-in-the-middle interface to get quick feedback on whether or not we could actually operationalize this successfully and get an actual business lift from it without having to spend a lot of time building custom code components. Automation here was a platform that allowed us to not only create AI models but allowed us to operationalize them and use them for a complete end-to-end process that drove business outcomes.

I think actually it would be less the features and the way the large components of the system work together. So for me, I could have always used Amazon's interface to be able to do human in the middle, but I don't have that highly integrated with all of the other components of the system. The ability, the AI models, the results, and other data sources, including the ones from the humans, are all within one flow and do not have to have any orchestration layer on my side via custom code components.

The problem many data scientists face is they fundamentally have to create machine learning models in a vacuum using snapshots of data. But the real world is messy.

World applications working with real-world customers are messy. And what Automation Hero allows us to do is to be able to either clean up the data or add things to the data to make the outputs of the models better, or it takes the outputs of those models and allows us to make it in a form that works better for the messy world we have to take the results into.

One of the things I'm really excited to continue working with Automation Hero on is their ability to be able to, once we're at scale with a lot of automation processes running, really be able to dig into those processes and see if we can drive even more efficiency from them. Right now we're at the early stages but I'm really excited about what we can do together. It's a really exciting time at Kent and we're going to be growing into a lot of new states with a lot of new products and every one of those new states and products has new challenges. Either the data has different issues or there are different regulatory environments. And one thing that I look forward to partnering with automation here on is being able to solve those very quickly in order to launch all of these new states and products as quickly and efficiently as possible. One of the things that I really appreciated the most about automation here is how we've been able to identify problems and gaps in both sides of the equation and be able to come together to solve those problems that have led to really great results for Kim.