From Google to Insurtech: Tensorflight’s Journey of Finding Product-Market Fit in an Old-School Industry

Discover how Tensorflight leveraged computer vision expertise from Google to revolutionize commercial property insurance, finding product-market fit through industry immersion and customer-driven development.

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From Google to Insurtech: Tensorflight’s Journey of Finding Product-Market Fit in an Old-School Industry

From Google to Insurtech: Tensorflight’s Journey of Finding Product-Market Fit in an Old-School Industry

Trading algorithms for underwriting manuals isn’t a typical career path for Google engineers. But in a recent episode of Category Visionaries, Tensorflight co-founder Robert Kozikowski revealed how his experience at Google and quantitative hedge funds shaped his approach to disrupting the insurance industry.

The Tech Background

Before founding Tensorflight, Robert’s career spanned both big tech and finance. “I was a software engineer working at Google, some Quantif hedge fund in London, US,” he shares. “In Quantif hedge fund, I work in a data analytics department, and we’re trading based on data. And this kind of was part of what realized how high the value just data in financial industry has.”

His first exposure to insurance came at Google, where he “work at Google Confer, that was product that’s well, now it’s discontinued, but were using machine learning to rank insurance quotes.”

The Unconventional Path to Industry Knowledge

Rather than following traditional startup advice, Robert took an unusual approach to understanding his market. When asked about influential books, he responds: “The book is underwriting commercial property… you look all of the startups advice and they say you have to really understand your customer, but you should be reading a book about understanding your customer, not the book telling you that you need to understand your customer.”

Finding the Right Problem

Tensorflight’s journey to product-market fit wasn’t straightforward. The company started with drone analytics in 2016, but quickly realized the limitations of this approach. As Robert explains, “If you apply AI, you’re kind of making money on scale. And the problem with drones is that there were just too few drone flights, too much challenges with drone flights to really make it that computer vision startups would succeed.”

This led to a pivot toward satellite imagery and a focus on insurance. The problem they discovered wasn’t new – insurance companies had been struggling with property assessment for years. The challenge was making the solution work at scale.

The Technical Reality of Insurance

The complexity of insurance underwriting presented unique technical challenges. Robert notes, “There are so many different shapes and sizes, year built standards which geocoding, or in case of commercial properties, you can have multiple buildings per address, multiple addresses per building. So just even figuring out what needs to be insured is a challenge.”

Building Trust Through Deep Industry Focus

Rather than trying to serve multiple industries, Tensorflight focused exclusively on insurance. Their approach was radically customer-centric: “We’re joking that our product management department is our customer underwriter… we just kept talking to a few companies and basically just building what they told us.”

This focus helped them differentiate from competitors. As Robert explains, “We differ within this category by firstly, we focus more on commercial properties than just residential. We also focus on whole structure, not just roof… we’re also more global.”

The Path to Product-Market Fit

The journey from idea to product-market fit took several years. Robert notes, “The problem didn’t change by a lot, but there was really the kind of real market fit maybe last two years.” This period was spent refining their solution to handle the complexities of commercial property assessment.

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