Leo Bernstein CEO and co-founder of LineSlip Solutions, talks about funding his MVP, acquiring the first customers, and moving from zero to product-market fit. Listen to the podcast for more details.
LineSlip Solutions leverages artificial intelligence (AI) and machine learning to obtain and design insurance data to automate essential tasks and control data vital to corporate insurance companies and commercial insurance brokers. He talks to Geordie about his journey in the entrepreneurship world.
What You’ll Learn What is structured data? How Leo went about analyzing the market and building his MVP Why many insurance businesses were unwilling to adopt technology What’s the initial problem that Leo and his team were trying to solve? How Leo started the company Challenges Leo faced while searching for a CTO Why Leo decided to adopt full-time online operations When did Leo realize product-market fit? Importance of marketing and creating brand awareness In this Episode: The creation of commercial insurance has been a manual process in many companies for many years. Leo says he made this discovery when he was a real estate investor responsible for acquiring commercial insurance for the company’s properties.
Nothing could have prepared him for how manual the entire process was. He would later spend close to one year wondering and interacting with people in the insurance industry. During that time, Leo realized that many players in the insurance industry struggled with the manual operations problem.
Commercial insurance companies dealt with structured data without treating it as structured data. Leo explains the meaning of structured data in this podcast.
Leo narrates the story of how he had attended an insurance renewal meeting and sought to review a few proposals from the carriers. During discussions with his broker, he (the broker) suggested a particular package and Leo sought to see the available alternatives. Unfortunately, the broker could not do so because he could not access the data.
Leo found that strange, especially coming from a data-driven industry. Listen to the podcast for the discussion Leo had with the agent and how thought-provoking it turned out to be for him.
Leo would later spend lots of time talking to people in the insurance industry and trying to understand why the problem existed in the first place. He could not figure out why the data-driven sector could not access its data. Later on, Leo discovered the insurance industry had good reasons for that. He explains in the podcast. Leo would later understand why the problem existed, and he explains in the podcast.
While on his inquiry process, Leo met different people, one of whom he had known through a close friend. He ended up meeting another person who happened to be a senior call producer at Marsh McLennan who introduced him to someone at AIG.
The team of three brainstormed the problem and tried to figure out whether technology could or could not fix some of these problems. Leo convinced his team that technology could help solve the issues. Apart from Leo, his colleagues were technically co-founders even though they did not join the company full time.
In 2018, Leo met Glen, someone he knew socially through his real estate partner. Through their interaction, Glen, an insurance guy, told Leo about a problem he was facing. It turned out that one of Glen’s largest customers wanted to see the entire commercial insurance data of their portfolio companies. Leo explains the problem comprehensively, and you cannot afford to miss this part of the podcast.
Leo knew he could help Glen, and he told him so. Listen to the podcast to understand how Leo helped Glen fulfill his customer’s demands and how he would later become one of his biggest customers. He also discusses industry lines of business. What are they? Get extensive details from the podcast. Leo talks about the initial idea the team had. This is a comprehensive