Akridata's solution for Data Curation for Labelling is designed to simplify the cumbersome process of sifting through large visual data sets and building effective training sets. Inherent complexities in dealing with video streams, like handling multiple frames per second and ensuring diversity in the selected frames, are easily managed with Akridata's advanced techniques. The traditional primitive methods of downsampling or random sampling which often miss out on valuable information are replaced with a more methodical approach. This process helps in building a diverse and valuable collection, thus enhancing model performance.