Video recruitment solution provider Cammio has introduced a new disruptive feature in its Cammio platform. The newly introduced Xpress Analytics™ brings Artificial Intelligence and Machine Learning to Video Recruitment. By extracting a unique combination of tonality and linguistic analysis, Cammio sorts the talent pipeline with a real-time analysis of candidates’ personality profile. Every new hire on the Cammio platform is fed into the matching algorithm to give it a deep learning capability to predict the right match with more accuracy and less bias.
Bas Dirkse, CTO and co-founder Cammio, further explains: “Cammio was born out of the passion that a CV does not do full justice to candidates. A CV is a one-dimensional historic view on job experience and education that totally ignores the critical aspect of personality that determines actual performance in a job. By adding a Cammio video interview early in the selection process, a more complete profile of candidates is created. The structure in the video interview has helped our clients to improve predictability and reduce bias in a highly efficient, personalized process. Evaluation of candidates has remained a manual process though. With Xpress Analytics, we can now provide an intelligent learning process based on a large number of data points that we collect in the video interview. This helps sort candidates based on a matching algorithm, guiding both recruiter and hiring manager in the selection process. This is a truly disruptive development in talent acquisition.”
Existing employers on the Cammio platform are provided access to the Xpress Analytics™ functionality from August 2017 as part of their all-inclusive license plans. Functionality includes individual profiles based on the reputable BIG5 model that are matched against learning profiles on company level or individual vacancy level. This is in line with the core Cammio principle to always provide clients with the most innovative and market-leading functionality available. Clients may also generate custom predictive models based on an in-depth analysis of existing candidate data. All candidate data is anonymized to comply with the highest data privacy standards.