Case study
AI-Powered Matching Platform
An AI-driven platform designed to eliminate bias and streamline candidate selection in the hiring process.
01 — Introduction
Upon client's request and recognizing an opportunity for innovation, Inform Technologies worked to create an AI-driven platform designed to revolutionize the hiring process by eliminating bias and streamlining candidate selection. With a focus on removing personal information such as name, gender, nationality, age, and ethnicity from the equation, our platform aims to connect companies with the most suitable candidates based on essential criteria.
02 — The Challenge
Traditional hiring processes are often based on various prejudices, leading to less than ideal matches between candidates and companies. Addressing this challenge requires a sophisticated solution that automates CV processing, ensuring fair and efficient matches while considering important factors such as technical skills, experience, and location.
03 — The Solution
Our solution is a two-sided platform that caters to both companies and candidates. Companies can post job listings and specify preferences for potential candidates based on location, functionalities, and characteristics. Simultaneously, candidates fill out detailed forms, providing information about their skills, experience, and location preferences. The AI-driven system then analyzes these forms, excluding any sensitive information, and calculates a matching percentage.
The platform also allows companies to set a matching threshold for each job posting. Candidates surpassing this threshold become potential matches, and companies can proceed with further interviews. Additionally, candidates can proactively send requests to companies, but to do so, they need to earn credits based on successful referrals and registrations.
Key features
01
Two-sided platform
Separate experiences for companies posting roles and candidates seeking them.
02
Bias-blind matching
Personal identifiers — name, gender, nationality, age and ethnicity — are excluded before candidates are matched.
03
Configurable matching threshold
Companies set the minimum match percentage required before a candidate surfaces for review.
04
Credit-based candidate outreach
Candidates earn credits through referrals and registrations, which they can spend to proactively contact companies.
Technical challenges we solved
Not publicly documented for this project.
Results & impact
Specific metrics have not been publicly disclosed for this project.
Benefits
Reduced Bias
By excluding personal information, our platform promotes fair and unbiased candidate selection, fostering diversity and inclusivity in the workplace.
Efficient Matching
AI-driven analysis ensures that companies are connected with candidates who best fit their requirements, reducing the time and effort spent in the initial stages of the hiring process.
Proactive Candidate Engagement
Candidates have the opportunity to initiate contact with companies, creating a dynamic and interactive environment that benefits both parties.
Continuous Improvement
Companies can reject suggested matches, providing feedback and additional criteria to the AI. This iterative process helps refine the matching algorithm over time, leading to increasingly accurate results.
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