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Project Brief

The task at hand was a strategic migration of a traditional platform to a cloud-based recruitment solution. It involved a complete rewriting of the legacy codebase, software refactoring, data migration, and integrating microservices.

Transforming Recruitment at TalentTech India with AI-Powered CV Screening and RPA

Background TalentTech India, a mid-sized technology consulting firm, specializes in providing cutting-edge IT solutions to clients across various industries. With a rapidly growing client base, the firm faced challenges in scaling its recruitment process efficiently. The traditional hiring approach was time-consuming, labor-intensive, and prone to biases, leading to prolonged hiring cycles and inconsistent candidate quality.

Challenge & Solution

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Challenges TalentTech India identified several key issues in their recruitment process:

Manual CV screening required HR personnel to review hundreds of applications per job posting, leading to delays in hiring.

Subjective evaluations resulted in inconsistent hiring decisions, affecting team performance and project execution.

Human recruiters, despite best intentions, often exhibited unconscious biases in candidate selection.

With business expansion, TalentTech India struggled to scale recruitment efforts while maintaining efficiency.

Solution

Background TalentTech India, a mid-sized technology consulting firm, specializes in providing cutting-edge IT solutions to clients across various industries. With a rapidly growing client base, the firm faced challenges in scaling its recruitment process efficiently. The traditional hiring approach was time-consuming, labor-intensive, and prone to biases, leading to prolonged hiring cycles and inconsistent candidate quality.

Key Features of the AI & RPA System

RPA bots were deployed to scan job applications and fetch resumes based on predefined criteria.

AI algorithms compared candidate profiles against job descriptions for better alignment.

Neutralized demographic and personal information, focusing purely on skills and experience.

Provided a ranked list of the best-fit candidates, helping recruiters prioritize top talent.

The system seamlessly integrated with TalentTech India’s existing Applicant Tracking System (ATS) for streamlined workflow management.

Implementation Process

RPA bots were deployed to scan job applications and fetch resumes based on predefined criteria.

AI algorithms compared candidate profiles against job descriptions for better alignment.

Neutralized demographic and personal information, focusing purely on skills and experience.

Provided a ranked list of the best-fit candidates, helping recruiters prioritize top talent.

The system seamlessly integrated with TalentTech India’s existing Applicant Tracking System (ATS) for streamlined workflow management.

Results And Impact

RPA bots were deployed to scan job applications and fetch resumes based on predefined criteria.

AI algorithms compared candidate profiles against job descriptions for better alignment.

Neutralized demographic and personal information, focusing purely on skills and experience.

Provided a ranked list of the best-fit candidates, helping recruiters prioritize top talent.

The system seamlessly integrated with TalentTech India’s existing Applicant Tracking System (ATS) for streamlined workflow management.

Conclusion

Conclusion By integrating AI-powered CV screening with RPA-driven resume extraction from Naukri.com, TalentTech India successfully transformed its recruitment process, achieving efficiency, quality, and fairness in hiring. The firm now has a scalable and data-driven hiring framework that aligns with its long-term growth objectives. This case study highlights the power of AI and automation in modern recruitment and serves as a model for other Indian organizations seeking to optimize talent acquisition.

This case study examines how TalentTech, a mid-sized technology consulting firm, revolutionized their recruitment process by implementing an AI-powered CV screening system. The transformation resulted in a 75% reduction in time-to-hire and a 40% increase in candidate quality, while significantly reducing hiring biases.