The funding follows an earlier private round and values the custom bicycle maker at £3.7m.
"Cyclists these days know they can get more from their equipment if it’s designed around their body, for the specific riding they do. Our recent reviews prove we can compete on performance terms with any of the big brands. This new funding will help us shift more people away from mass-produced equipment in favour of a better bike with perfect fit, fabrication, and finishing designed for individual performance needs."
The funding round was led by Hambro Perks with participation from international backers across three continents.
"We have seen a clear societal shift towards remote work, a trend that we believe will continue to accelerate at pace. We should embrace the opportunities that this presents, and platforms like Virtual Internships help students and graduates from around the world to acquire new skills that will accelerate their career growth and prepare them for the 21st-century workplace. Our mission is to bridge the gap between education and the workforce by making global remote work experience accessible to all in a digital and borderless world."
The Series B round was co-led by Wavecrest Growth Partners and Espresso Capital to support ongoing corporate expansion.
"At the most basic level, companies deliver value to customers through the combined effort of their internal teams and suppliers. Companies often under-invest in the supplier experience – in making it easy for suppliers to deliver their best work. However, we’ve seen a significant shift recently and many of the world’s leading brands are investing in technology to address this and to become ‘customer of choice’ for as many of their suppliers as possible."
Project A led the seed round to help Valve scale its flexible workspace platform across North America and EMEA.
"Valve makes it easy for companies building flexible workspace businesses – whether you are a landlord, operator, brokerage, application or tech entrepreneur,"
The seed round was co-led by Merus Capital and GoHub Ventures with backing from APX and Techstars.
"training latest deep learning models accurately on very small data sets, which any local, low-resource language usually suffers from, is a very challenging task because models are usually designed to perform well in English with terabytes of available text and speech data but not in languages with a few hundred megabytes of data."
