Revolutionizing Recruitment with People Recruit

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People Recruit leverages fine-tuned open-source language models to achieve high-accuracy job description (JD) to CV matching. Our advanced NLP techniques ensure precise and relevant candidate-job alignments across diverse industries.

State-of-the-art Models for Comprehensive Matching

Our models are trained on a substantial amount of resumes and job descriptions (JDs), ensuring they work effectively on various types of CVs, including long CVs, columnar CVs, and short CVs, with high accuracy.

What is JD-CV matching?

JD-CV matching is the process of comparing a job description (JD) with a candidate's resume (CV) to assess the suitability of the candidate for the job. This involves analyzing various parameters such as skills, location, work experience, role, domain expertise, and job stability. By using advanced techniques like natural language processing (NLP), JD-CV matching aims to ensure that the most relevant and qualified candidates are identified for a given job opening.

Key Features

At PeopleLabs.ai, our matching engine focuses on extracting the following key attributes:

Skills

We match JDs to CVs based on proximate skills, not just direct skill matches. For example, a candidate with expertise in Java and Spring Boot will be matched to a job requiring Java and Hibernate due to the related skillsets.

Total Experience Match

This feature evaluates the overall years of relevant work experience to ensure alignment with job requirements.

Certification Match

We ensure alignment between job descriptions and candidate resumes by verifying the presence of relevant certifications as specified in the JD.

Location Match

The location match ensures alignment between job locations and candidate preferences, accommodating variations such as recognizing 'Bengaluru' and 'Banglore' as the same location.

Domain Match

Domain to domain match ensures alignment between a candidate's industry expertise and the specific sector requirements of a job.

Education Match

Educational requirement matching aligns job roles with candidates' academic qualifications efficiently.

Job Title Match

Job title match assesses the alignment between a candidate's previous roles and the job title requirements.

Job Hops

The number of job hops criteria evaluates a candidate's job stability based on the frequency of their employment changes.

How Does Our Matching Engine Work?

1. Parsing JDs and CVs: The first step of our matching engine involves parsing both job descriptions (JDs) and candidate resumes (CVs). Parsing involves extracting key information such as skills, work experience, education, and other relevant details from the text.

2. Utilizing Fine-tuned Language Models: In the parsing stage, we utilize fine-tuned language models (LLMs) to accurately extract information from JDs and CVs. These LLMs have been specifically trained and optimized for understanding job-related text, ensuring precise extraction of relevant data.

3. Storing in the Database: Once the information is extracted, it is stored in a structured format in our database. This allows for easy retrieval and manipulation of data during the matching process.

4. Scoring Algorithm: After parsing the JDs and CVs, we employ a scoring algorithm to evaluate the compatibility between a candidate and a job. This algorithm takes into account various factors such as skill match, work experience, education level, location, and other relevant criteria specified in the JD.

5. Generating Final Score: The scoring algorithm analyzes the parsed information from both the JD and the candidate's CV, assigning numerical scores to each relevant aspect. These scores are then combined to generate a final match score, indicating the degree of compatibility between the candidate and the job.

Benefits of Using Our JD CV Matching Engine

1.State-of-the-Art NLP Models: Our engine utilizes cutting-edge Natural Language Processing (NLP) models, which are trained on a custom dataset. This ensures accuracy and relevance in matching job descriptions (JDs) with CVs.

2.Reduction in Manual Screening: By automating the screening process, our engine significantly reduces the need for manual screening, thereby boosting team productivity. This frees up valuable time for HR professionals to focus on other critical tasks.

3.Cost Effectiveness: Unlike systems that rely on expensive chat GPT models, our engine offers cost-effectiveness without compromising on quality. This makes it accessible to businesses of all sizes, allowing them to streamline their recruitment process without breaking the bank.

4.Screening on Multiple Parameters: Our engine goes beyond basic keyword matching. It screens CVs against JDs on multiple parameters, including skills, experience, education, and more. This comprehensive approach ensures that only the most suitable candidates are shortlisted.

5.Easy Upload and Reporting: With just two clicks, users can upload JDs and CVs effortlessly. The engine then generates a final matching scores Excel report, providing a clear overview of candidate suitability. This simplicity saves time and enhances the overall recruitment experience.