Public Funding for BKWAI Ltd

Asset monitoring is increasingly based on real-world data, helping to make assets safer and optimise their performance. The Queensferry Crossing bridge, for example, uses data from over 1,000 sensors to monitor weather and motion in real time and identify performance issues before they become problems. A data-driven built environment whilst delivering significant value across an asset's life-cycle -- from planning through to asset construction and preventative maintenance, can only be fully exploited if the full range of data sources available are utilised and the correct intelligence applied. Based on the current manual interpretation of generated data by trained engineers or even when supported by monitoring technologies such as ground movement monitoring or digital twin applications, whilst added significant value, still fail to provide a complete picture of a given asset. It is this known market gap in data use that BKWai seek to address. Combining emerging innovations in big data and deep learning, BKWai aim to develop the first solution that will enable the automatic extraction of key insights from large scale datasets derived from the abundance of on-site sensor networks, satellite imagery and third-party data for infrastructure asset monitoring to provide real time engineering intelligence within the built environment.