AI-Powered CV Matching

title=AI-Powered CV Matching

About the Client:

Industry:

Recruitment

Location:

Silicon Valley, California

Duration of the Project:

4 months

The project’s main goal was to enhance the recruitment process’s efficiency by leveraging AI to accurately match candidate CVs with job vacancies. The system aimed to minimize manual screening effort and improve the match quality between job requirements and applicants’ qualifications.

Team Involved:

Part-time Project Manager

Part-time AI/Machine Learning Lead

AI/Machine Learning Engineer

Full-stack NodeJs/ReactJs Engineer

What business tasks did the client want to solve?

Streamline the Recruitment Process

Implement an efficient system to automate the screening of CVs, reducing the time and effort involved in the initial stages of recruitment.

Improve the Quality of Hires

Utilize AI to more accurately match candidate qualifications with job requirements, thereby enhancing the caliber of candidates shortlisted for interviews.

Increase HR Operational Efficiency

Reduce the workload on HR personnel by automating the CV matching process, allowing them to focus on more strategic tasks.

Data-Driven Recruitment Decisions

Leverage AI and analytics to make informed hiring decisions based on comprehensive data analysis.

Scalable and Adaptable Solution

Develop a system that is scalable for varying business sizes and adaptable to different industry needs.

What pitfalls did client face?

Lack of Specialized AI Expertise

The client did not have an in-house team with the necessary expertise in AI and machine learning, crucial for developing such a sophisticated system.

Complexity in Integrating Diverse Data Sources

Integrating various formats of CVs and job descriptions from multiple sources presented a significant challenge.

Data Privacy and Security Concerns

Ensuring the confidentiality and security of sensitive personal data contained in CVs was a major concern.

High Costs of Advanced AI Solutions

Developing a state-of-the-art AI solution can be expensive, especially when involving cutting-edge technologies like OpenAI’s API.

What we suggested

Business Analysis

Mind map creation to visualize the project scope and requirements.
Detailed task descriptions including functionalities and user flow.
Development evaluation for resource and time estimation.

Project Set-up and Service Architecture

Backend: NodeJS was used for the backend, implementing a micro-service architecture for scalability and independence of services. AWS provided robust cloud infrastructure ensuring data security and system reliability.

Frontend: A responsive and user-friendly interface was developed using React, with Redux for state management and Redux-Saga for handling side effects. Typescript was used for code clarity and reliability.

Development

Team scaling to include AI specialists, backend and frontend developers.
Integration with ATS for CV retrieval and database for job descriptions.
Development of the matching algorithm using OpenAI API.
Creation of the first functional version within a planned timeline.

Support

Ongoing integration of the new features and system updates.
Expansion of the system`s use in the client`s HR processes.
Continuous support and maintenance of the solution.

Technical architecture

The technical architecture of the AI CV Matching System involved integrating OpenAI’s API, Node.js, AWS, and database management to create a robust and efficient solution:

OpenAI API

Used for natural language processing and machine learning. The API was crucial in parsing CVs and job descriptions, understanding the context, and extracting relevant information for matching.

NodeJS Backend

Served as the backbone of the application. Node.js handled requests and responses between the user interface and the server, and it processed the data fetched from OpenAI API and the databases.

AWS for deployment

The application was deployed on AWS, providing a scalable and secure cloud environment. AWS services ensured the system’s high availability and performance, managing large data volumes and user requests efficiently.

Connection to client ATS

The system connected to the client’s Applicant Tracking System (ATS) to fetch CVs. This integration was crucial for automating the data input process, ensuring that the latest candidate information was always available for processing.

Database with job descriptions

A dedicated database stored job descriptions. This database was regularly updated and used by the AI system to understand the requirements of each job opening.

Storing matching results in database

After processing, the results of the CV-to-job description matching were stored in a database. This enabled HR teams to access and review the matches, track the effectiveness of the system, and make informed recruitment decisions.

Business Outcomes

50% reduction in recruitment time

Significant improvement in the quality of hires

Reduced hiring costs by 30%

Enabled data-driven hiring decisions

Enhanced candidate experience