THE ECONOMIC EFFICIENCY OF AI-DRIVEN TALENT ACQUISITION

Mariia Artemenko; Sofiia Vasylyeva; Tymoshenko Mykhailo; Larysa Liashenko

doi:10.36074/grail-of-science.01.05.2026.033

THE ECONOMIC EFFICIENCY OF AI-DRIVEN TALENT ACQUISITION

Authors

Mariia Artemenko Taras Shevchenko National University of Kyiv, Ukraine https://orcid.org/0009-0002-2534-6086
Sofiia Vasylyeva Taras Shevchenko National University of Kyiv, Ukraine https://orcid.org/0009-0004-7604-6141
Tymoshenko Mykhailo Taras Shevchenko National University of Kyiv, Ukraine https://orcid.org/0009-0007-8763-7600
Larysa Liashenko Taras Shevchenko National University of Kyiv, Ukraine https://orcid.org/0009-0008-6464-3167

DOI:

https://doi.org/10.36074/grail-of-science.01.05.2026.033

Keywords:

NLP, FastAPI;, Recruitment ROI, Decision Support Systems, Talent Acquisition

Summary

Investigated the critical inefficiency of manual recruitment in the technology sector, where HR professionals spend over 50% of their working time on routine document processing, causing recruiter fatigue and hiring errors costing up to six months of a developer's salary. Developed an automated Decision Support System (DSS) built on FastAPI, PyMuPDF, and the Nemotron-3 large language model, transforming unstructured PDF resumes into a structured Candidate Scorecard with a Match Score, Skill Gap Analysis, and targeted interview questions. Proposed a Speed-Dating Bot module that increases candidate reply rates by 40–60% through NLP-driven personalized outreach. Processing a vacancy with 50 candidates decreases from 25.5 to 2 hours, yielding approximately $700 in savings per vacancy at near-zero operational cost.

Confirmed full GDPR compliance through Docker containerization with in-memory-only data processing.

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References

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