Autonomous Innovation Concept Engine - Demo

At its core, this new capability enables a revolutionary way to accelerate the velocity and efficiency of the product development, design and manufacturing process across fashion, apparel and accessories. This autonomous innovation engine is a new capability powered by artificial intelligence and machine learning that's also fueled by an always on stream of external and internal data. This not only provides us with real time insights into the latest sociocultural trends, but also autonomously generates themes and inspiration that powers this engine to generate hundreds of new designs with the click of a button. It also provides a way to synthetically test potential designs with consumers and also provide real time recommendations on the optimal suppliers and manufacturers to work with for potential designs. Let's take a closer look at how this works. First, we have the ability to toggle on and off specific internal and external data sources that we want to use to generate real time insights. These could include external data sources like social listing across different social media platforms, trends research and beyond. It also includes internal data sources such as our sales data, website analytics, and much more. Once these inputs have been selected, the engine synthesizes the latest sociocultural trends that consumers across the world are experiencing. In turn, this is further synthesized into key fashion themes that are relevant for our brand and our audience. Selecting any one of these fashion themes will then allow us to instantly create a set of uniquely tailored mood boards that are uniquely curated to our consumers demographic and occasion. Selecting any one of these mood boards will then prompt the engine to generate dozens of new designs with the click of a button. These designed renders are based off of real time socio cultural trends that have been further designed to fit a specific fashion theme. The engine also features an option to curate a microdrop, which effectively is just a mix of apparel and accessories that are uniquely paired together to fit a common aesthetic. We can also see how consumers might react to some of these designs through a feature called synthetic testing. This allows us to directionally get feedback from synthetic consumers that have been trained to mimic the behaviors, expectations and values of our different consumer Personas. And this effectively then allows us to rapidly iterate and improve on these designs in real time before any investment in real design and manufacturing have actually been made. Lastly, once we're happy with the design, the engine scans through all the various suppliers and manufacturers that we've typically worked with and prioritizes the key ones that are most cost effective, efficient and trusted for us to potentially work with.