Save Time with AI-Driven Talent Matching

The match score evaluates the compatibility between a candidate and a specific job. It aggregates the candidate’s work history, skills, seniority, education, and other structured attributes to predict the likelihood that the candidate would be a good fit for the job. The score mimics how a recruiter might assess a candidate, but in a standardized, efficient, and data-driven way.

Higher scores indicate closer alignment to job requirements:

• Very High: Strongly recommended
• High: Good fit
• Medium: Moderate potential
• Low: Poor match, likely not actionable

Each candidate is independently evaluated on several dimensions:

• Skills match (from resumes vs. job descriptions
• Work experience (title, seniority, trajectory)
• Education and certifications
• Career progression patterns
• Job title relevance

Each dimension is scored using separate models and then ensembled into a single weighted score. The weights reflect the relevance of each dimension for that specific job, based on real-world hiring outcomes.

A variety of AI techniques and disciplines are used to analyze candidate profiles and job descriptions, evaluate the compatibility between candidates and roles, and provide explanations for the matching process.

These techniques include:

• Natural Language Processing (NLP): Since individuals represent themselves through written content such as resumes and cover letters, NLP helps convert human language into structured categories that can be processed by automated systems.
• Deep Learning: With access to extensive data on career paths, job applications, and hiring patterns, deep learning models help identify trends and insights into how candidates progress through their careers and how employers make hiring decisions.
• Generative AI: This enables the presentation of results in clear, natural language, making insights more accessible and easier to interpret.