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Transforming Personnel Management: Automating Candidate Selection for Project Success

Discover how our custom-built microservice, powered by Large Language Models (LLMs), streamlines employee recruitment by automating candidate selection for projects. Through innovative approaches like semantic search and customizable relevance filtering, our solution enhances efficiency, accuracy, and adaptability in assembling high-performing project teams, driving success in personnel management.


In the modern world, successful personnel management is one of the key factors for any company’s success. Employee recruitment stands as a central aspect of this process. Significantly streamlining this facet can be achieved through process automation.

The main objective of such an approach is an automated selection of best-suited employees for current or potential projects. It enhances the recruitment process, reducing time spent on candidate search and selection while increasing the likelihood of project success, as tasks become more efficiently regulated with more suitable resources and competencies employed. In addition, automation accelerates the analysis and resume screening process, ensuring the prompt assessment of potential candidates and the project team formation.



Challenge#1. Dealing with diverse resume formats and styles:


The main challenge was the efficient extraction and collection of information from resumes, which come in all shapes and formats. The use of traditional approaches to data extraction did not bring the expected results due to the specific character of CVs. To solve this issue, we utilized Large Language Models (LLMs), AI-powered tools capable of understanding and processing human language. This enabled us to extract necessary information much more effectively despite differences in format.

Challenge#2. Ensuring accurate semantic search:


Another challenge was developing a semantic search capability that could efficiently and accurately identify relevant information within a large dataset of resumes, taking into account the nuances of language and context. Using classic search methods proved insufficiently accurate. We adopted a novel approach to address this limitation: storing resume data in vector format. This enabled rapid and precise searching via vector similarity scoring and allowed us to more accurately determine the connections between queries and resumes, considering their semantic nearness.



Our approach to processing resumes and project descriptions underwent significant transformations as we refined our methodology. Initially, we followed a classical method, extracting and storing resumes and project descriptions in a relational database. However, as the project moved forward, it became apparent that this approach was not well suited to handling unstructured data like CV text. To address these limitations, we implemented LLMs to extract relevant information, resulting in a significant increase in our ability to process resumes and project descriptions effectively.

We developed a process for vectorizing the collected information and storing it in a vector database. This enabled us to rapidly and flexibly execute candidate filtering based on specific job opening requirements.

However, despite this improvement, the process of working with filtered candidates still required considerable manual effort from the recruiting department. To further optimize our approach, we added a Virtual Assessment layer, which leverages LLMs to analyze how well a specific candidate is suited for a specific job, providing a short explanation to support the evaluation.


Our solution is a custom-built microservice that leverages Large Language Models (LLMs) to automate candidate selection for projects. This innovative approach enables efficient information extraction, semantic search, and accurate decision-making.

The microservice utilizes LLMs to analyze large datasets of resumes and project descriptions, extracting relevant information such as skills, experience, and education. By storing this data in vector format, we enable rapid and flexible candidate filtering based on specific job openings.

A key advantage of our solution is its customizable relevance filtering. Users can adjust the importance of various criteria — such as knowledge, experience, education, and skills — for each project, ensuring that the most relevant candidates are selected. This flexibility enables the microservice to provide a tailored set of candidates for each project, facilitating the rapid creation of high-performing teams.

Technical Highlights:

  • Our microservice employs state-of-the-art LLMs for information extraction and candidate assessment.
  • Data is stored in vector format enabling rapid and accurate candidate filtering through semantic search via vector similarity scoring.
  • The system features customizable relevance filtering, allowing users to adjust criteria weights (e.g., knowledge, experience, education, skills) for each project.


Building on our success, we’re committed to continued innovation and improvement. Our current focus is on enhancing the Virtual Assessor module, which leverages Large Language Models (LLMs) to assess candidate suitability. We are working on further extending its flexibility and capabilities to provide up to 25% more accurate candidate results and detailed evaluations of each candidate’s strengths and weaknesses regarding project requirements.

To further streamline our solution, we’re planning to integrate it with our internal deal management system and an external personnel management platform. This will enable seamless data sharing, reduce manual effort, and increase the overall efficiency of the recruitment process.

In tandem, we’ve provided comprehensive training for our staff, ensuring a smooth transition and effective adoption of these enhancements into their workflow processes. As a result, we’ve significantly enhanced our ability to identify top talent, accelerated the staffing process, and assembled high-performing project teams that consistently deliver exceptional results.

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