Researchers at the Russian Academy of Sciences and the St. Petersburg Federal Research Center have unveiled a neural network based tool designed to help companies and HR teams enhance the quality of conversations with sales managers. The initiative was shared with socialbites.ca by the academy and outlined at the St. Petersburg facility.
According to Alexey Kashevnik, a senior researcher at the Integrated Automation Systems Laboratory within the SPb FRC RAS, the online application guides recruiters to spot top applicants during interviews with sales professionals. The software analyzes the candidate’s interview behavior to map key personality traits that predict success in the field, based on video footage of the interview. The goal is to distill a reliable profile of the strongest sales performers from real-time interactions (Source: Russian Academy of Sciences).“
Sales management remains one of the most in-demand professions, with company performance often tied to the capability and drive of these workers. As a result, HR tools that can reliably identify and retain high-caliber sales talent hold significant value for organizations across North America.
In practice, the hiring process begins with a short video presentation from the applicant. The candidate answers a few prompts such as a personal introduction, a recap of relevant experience, and reasons for pursuing a role in service or sales. The submission window is limited to roughly three minutes.
Once uploaded, the app processes the recording with a neural network that assesses personality indicators across five measurable dimensions relevant to sales leadership: persistence, empathy, optimism, initiative, and adaptability during interactions.
Kashevnik notes that early results from dozens of interviews show a strong alignment between the app’s assessments and the judgments of experienced HR professionals. To improve accuracy, the team plans to expand the training dataset substantially, enabling the model to learn more nuanced patterns in candidate behavior. The project remains actively seeking partners for further development and the rollout of this decision-support system in practice (Source: Russian Academy of Sciences).”
Earlier work by researchers touched on advanced technologies such as electromechanical actuators and artificial muscle concepts powered by alternating current, indicating a broader experimental environment where AI-driven tools intersect with hardware-oriented research.