Back in 2018, we started Vade with a simple goal: make parking more convenient. To us, that meant helping drivers find open parking, a problem so ingrained in society that nearly everybody accepts the struggle as a part of life. Little did we know, we were embarking on a long journey of learning, R&D, more learning, pivoting, more R&D, even more learning... you get the idea. At times it felt frustrating to take steps backwards, but it's all part of the journey and we're much better off today because of it.
Our formative years produced two key realizations that have and continue to define Vade. The first is that the key to automation in this space is data. Not just any data, we're talking about real-time curb space occupancy data. Cities are starting to catch up with on the supply side with static data including the type, location, and rules of different types of curb spaces in their inventory, but they still have no visibility into the demand side.
That realization came when we stumbled across the infamous SFpark pilot that quantified the immense benefits of integrating sensor-based occupancy data. Those results included a 43% reduction in search times for drivers, 30% reduction in emissions, 23% increase in sales tax collections, and many others. Almost a DECADE later, real-time occupancy data is still yet to reach mass adoption. That leads to our second key realization: eliminating barriers to adoption is a higher priority than pushing the boundaries of functionality.
Today, we've developed patent-pending IoT cameras that use solar power and LTE to eliminate the need for any power or network infrastructure. Once installed, the cameras capture and transmit encrypted images to our server once every minute, where custom computer vision is applied to produce real-time curb space occupancy data. The end-product of our solution is our Real-Time API, packed with functionality to make it easy for anyone to access and integrate real-time data.
The flexibility of our hardware and the complementary nature of an API product are instrumental to eliminating the barriers to adoption, but we also need a go-to-market strategy that minimizes the hassle of municipal procurement and sales cycles. Fortunately, building our own hardware from scratch resulted in production costs being an order of magnitude cheaper than the alternatives, which enables us to use a pure SaaS model and provide the hardware to cities with no upfront cost.
We started focused on smart parking and integrating with parking apps to show real-time availability, enforcement solutions to improve public safety, and GIS analytics platforms so cities can make better informed decisions. However, especially in the wake of COVID, curbs are used for more than just parking. In addition to smart parking, we expanded our focus to curb management as a whole including loading zone management, fleet management, demand-based pricing, and more.
We're building the future of urban mobility in smart cities by laying the groundwork and implementing infrastructure that will power innovation that's never been possible before. By starting with a narrow focus in markets with a clear value proposition, we're making it easy for any city to dip their toes in the world of dynamic curb data. Over time, we have the ambitious vision of creating a one-of-a-kind data science platform that truly bridges the gap between atoms and bits by enabling anyone to develop their own computer vision applications. Our goal is facilitate the creation of new categories of data and support a thriving public/private API economy, all while building robust security systems and protocols to provide the benefits of monitoring the physical world in real-time without the drawbacks of security concerns.
We've come a long way already, but there's a longer road ahead and we're just getting started.