Recruitment AI

The system enables the analysis of documents about candidates or employees, information classification, and the determination of candidate levels based on a set of established criteria.

Goal

• The customer company has created its own product that meets the needs for automating recruitment processes for large HR and recruitment firms. The product offers broader functionality, lower cost, and easier customization compared to similar systems provided by global operators.​

  Main Pain Points of Recruitment Companies​

  • Speed and Efficiency of Processing Large Amounts of Candidate Data​
  • Ensuring quick and efficient handling of extensive candidate data.​
  • Low Speed of Identifying Suitable Candidates​
  • Improving the speed of identifying suitable candidates for job openings.​
  • Using Human Labor to Screen Resumes​
  • Reducing the need for human labor to screen resumes and match them to job descriptions.​
  • Loss of Operational Efficiency Due to Manual Processes​
  • Addressing the lack of automated systems for real-time performance monitoring, which leads to decreased efficiency in recruitment processes.​
  • Limited Capabilities of Existing Systems​
  • Enhancing the capabilities of systems used for analysis, visualization of recruitment reports, and tracking candidate interactions and progress.

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Result 

Technologies

• • OS: Linux (CentOS, Red Hat, OpenSuSE)​
  • Databases: PostgreSQL​
  • Languages/Platforms: NodeJS, JavaScript​
  • Frameworks/Libraries:​
  • Express.js​
  • Various libraries for image and video recognition​
  • LVA transcription for audio analysis​
  • Servers: NodeJS server​

  SQA Standards:​

  • ISO 25010​
  • ISO/IEC 12119​
  • ISO/IEC 27000​

  Technical Environment:​

  • Docker​
  • Jenkins​
  • VM​

  Cloud Standards:​

  • Integration capabilities with various cloud services and APIs​

  Machine Learning:​

  • Utilized for facial expression recognition and voice analysis processes​

  Integrations:​

  • AI system​
  • AI Recruit system​