An Innovative and Revolutionary SME Machine-Learning AI-Powered Digital Stage, Predicted to Save 60+ Hours Per Role Cast

Flaircube Ltd（Flairbox）拥有Robert Eades，Elliot Janks和Peter Hickling的核心团队，正在为表演艺术专业人士提供革命性的远程连接和合作机会。Flairbox的平台将视频共享与机器学习相结合，允许演员轻松上传视频，演员导演可以有效地排序，每个角色演员节省60小时。该解决方案将帮助行业在COVID-19关闭后恢复和重组，并将为公平的铸造实践铺平道路。第3和第6个月，仍在研发中，同时为尽可能多的演员提供数字舞台，目标是在第6个月达到5，000。让这些演员有机会回到他们的职业，有地方表演，并被发现为他们的工作。到12个月，Flairbox将利用收集的数据，以支持他们的机器学习AI排名系统的第一次迭代，打破了多达7,500名演员的准入门槛，并为演员导演节省了60个小时。到18个月，Flairbox将拥有一个商业上可行的、机器学习AI驱动的排名系统，可供每一位英国选角导演和演员使用，目前已有12000名演员使用该平台表演和寻找工作。这项技术将为每个角色节省60多个小时，相当于为每个角色的制作人节省2,400多英镑（演员导演时间为40英镑/小时），同时消除目前基于教育，经验，位置和社会经济背景的演员之间的歧视。

Flaircube Ltd (Flairbox), with a core team of Robert Eades, Elliot Janks and Peter Hickling, is revolutionising opportunities for performing arts professionals to connect and work together remotely. Flairbox's platform combines video sharing with machine learning to allow actors to easily upload videos that casting directors can efficiently sort, saving 60 hours per role cast. This solution will help the industry recover and retool following COVID-19 closures and will lead the way for equitable casting practices.Months 3 & 6, still in R & D whilst providing a digital stage to as many actors as possible, with a target of 5,000 by month 6\. Giving those actors a chance to return to their profession with somewhere to perform and be discovered for their work.By month 12, Flairbox will be utilising collected data in order to power their first iteration of a machine learning AI ranking system that breaks down barriers to entry for up to 7,500 actors and saves up to 60 hours per casting for casting directors.By month 18, Flairbox will have a commercially viable, machine learning AI powered ranking system available to every UK casting director and actor, with 12000 actors already using the platform to perform and find work. This technology will save more than 60 hours per casting, equating to more than £2,400 saved for producers for every role (£40/hr of casting director's time), whilst simultaneously removing current discrimination between actors based on: education, experience, location and socioeconomic background.