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PLACIVIFY: An AI-Driven Placement Ecosystem with Multi-Model Orchestration and Voice-Interactive Interviews

Open Access
Published: 21 October, 2026
Pages: 482-498
  • Janani M. , Janani M.
    Assistant Professor, Department of Computer Science and Engineering, NPR College of Engineering and Technology, Natham, Dindigul, India
    Assistant Professor, Department of Computer Science and Engineering, NPR College of Engineering and Technology, Natham, Dindigul, India
  • Aswanthwin Fried S. , Aswanthwin Fried S.
    Student, Department of Computer Science and Engineering, NPR College of Engineering and Technology, Natham, Dindigul, India
    Student, Department of Computer Science and Engineering, NPR College of Engineering and Technology, Natham, Dindigul, India
  • Gowtham P. , Gowtham P.
    Student, Department of Computer Science and Engineering, NPR College of Engineering and Technology, Natham, Dindigul, India
    Student, Department of Computer Science and Engineering, NPR College of Engineering and Technology, Natham, Dindigul, India
  • Kalaiselvan A. Kalaiselvan A.
    Student, Department of Computer Science and Engineering, NPR College of Engineering and Technology, Natham, Dindigul, India
    Student, Department of Computer Science and Engineering, NPR College of Engineering and Technology, Natham, Dindigul, India
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How to Cite

M., Janani, Aswanthwin Fried S., Gowtham P., and Kalaiselvan A. 2026. “PLACIVIFY: An AI-Driven Placement Ecosystem With Multi-Model Orchestration and Voice-Interactive Interviews”. Recent Research Reviews Journal 5 (2): 482-98. https://doi.org/10.36548/rrrj.2026.5.015.
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Keywords

TF-IDF Vectorization
Semantic Similarity
LLM-Based Resume Screening
Multi-Model Orchestration
Voice-Interactive Interview
Talent Intelligence Engine
Circuit-Breaker Architecture

Abstract

Campus placement involves handling resumes, application forms, interviews, and company research on behalf of the candidates, whereas the placement team members have to deal with huge volumes of candidates. PLACIVIFY is an AI-based placement ecosystem aimed at integration of all these processes through one platform for the benefit of both students and the placement team members. The platform incorporates a hybrid ATS Resume Scoring solution that integrates local TF-IDF analysis and semantic evaluation powered by LLMs in order to determine resume-job match and skill gap. There is an AI-based Resume Assistant that helps in improving the experience in accordance with Action-Task-Result approach. The Company Intelligence Hub creates structured data for recruitment preparation, whereas the Talent Intelligence Engine uses methods such as similarity between the profile and job, skill expertise consideration, penalties for missing skills, and constraints on candidates’ eligibility for candidate classification and ranking. For increasing the operational reliability of the system, PLACIVIFY implements multi-model orchestration, local NLP fallback, caching, circuit-breaker, and JSON repair features. This system has been developed based on the MERN stack and delivered via web and Electron applications. Using the system with the help of student profiles and resume examples annotated by experts, it was found that there is less screening time, more consistent scores, and callbacks.

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