Breaking Boundaries to Fund A Better Future with Calibrate Ventures’ Kevin Dunlap

<p>Every solution starts with an idea. Consumers needed more home security, so the founders of Ring came up with the smart doorbell. Home owners had trouble finding time to complete recurring tasks like vacuuming, so Roomba automated the vacuum. But despite those great ideas, every founder needs some help along the way. Or more specifically, they often need financial backing to scale a business, and they need someone to believe in them when others won’t. </p><p><i>“We often joke that our best investments are the ones that nobody else likes. We're investors in Dollar Shave Club and I was told that was stupid. It was acquired for a billion dollars. With Ring, people laughed at me and they're like, ‘You invested in a doorbell business?’ A lot of times the ones that aren't necessarily obvious are really the best opportunities. So it was digging in and understanding if this does work, is it going to be really valuable?”</i></p><p><a href="https://www.linkedin.com/in/kevindunlap/">Kevin Dunlap</a> is Co-founder and Managing Partner of <a href="https://www.calibratevc.com/">Calibrate Ventures</a>, a VC firm focused on funding early-stage A.I. and automation companies. Every year Kevin and his partners sift through hundreds of companies looking to take the next step in their entrepreneurial journey. But what separates the companies that receive funding from the ones that never make it past the pitch stage? And what trends is he seeing within the automation and A.I. space? On this episode of IT Visionaries, Kevin explains Calibrate process for picking companies to partner with and what questions start-ups should be answering before they step up to pitch to VCs. Enjoy this episode.</p><p> </p><p><strong>Main Takeaways</strong></p><ul><li><strong>It’s All About Data:</strong> Data capturing and data labeling remains the two most important factors to developing functional A.I. solutions. When data is not effectively captured and/or labeled, the algorithm cannot effectively decipher what it needs to in real time. An example o