How does Element reduce costs and optimise its recruitment system at the same time?
Element — who are we?
Element (www.elementapp.ai) is a young, growing and dynamical company from Gdynia providing innovative solutions to automate the recruitment processes. The company mission is the implementation of the latest technologies to shorten the recruiting time and to reduce the cost of acquiring suitable candidates.
In the first quarter of this year, its founder, Maciej Michalewski, came to us. Maciej was looking for qualified annotators who could relieve his team and allow for scaling the construction of a machine learning solution by delivering lalabelled CVs at a rate of over 100 per day.
What did we?
After discussing the company's needs, for the needs of piloting, we provided a large number of annotators to finalize the data science. The team decided on the most effective composition according to the needs and the budgetary possibilities.
Effects:
Calculated for a business day, we have relieved the company of labelling tasks by approx. 100 CV per day in Polish and English. The company optimized its costs by paying only one FV for all services it received. It also gained resources, by relieving the departments of micromanagement and the entire project scalability.
Opinion of client:
The DataLabeling team approached our needs and budget possibilities very flexibly. In a short time, it was possible to set assumptions and goals, and then proceed to the task. Very good communication, understanding our expectations and a professional approach to our responsibilities make us recommend DataLabeling as a reliable business partner — Maciej Michalewski, CEO & Founder ELEMENT
Paweł Cyrta — specjalista ds. dźwięku, głosu, muzyki i multimediów. Doświadczony badacz i twórca oprogramowania specjalizujący się w analizie i przetwarzaniu sygnałów muzycznych, głosowych i dźwiękowych. Posiada obszerną wiedzę na temat systemów informatycznych, implementacji oprogramowania Open Source, Data Science, Data mining, Web mining, Text mining, NLP, Big Data, Machine Learning (HMM, GMM, SVM, ..., BDN, Deep Learning, ...). Dysponuje głęboką wiedzą z dziedziny dźwięku i rozwiązań audio, systemów emisji, przetwarzania, kompresowania i kodowania dźwięku. Są mu bliskie psychoakustyka, akustyka pomieszczeń, modelowanie 3D, programowanie i inżynieria dźwięku.