Sovren Inc. (company) is a privately held software-components-only firm established in 1996. Sovren develops and licenses the world’s best enterprise-grade Resume/CV Parsing and Semantic Matching / Artificial Intelligence Matching (AIM) components.

The Sovren Resume Parser is used within human resource software, and on recruitment web sites and portals, to simplify and accelerate the application process, to extract and classify thousands of attributes about the candidate, and to provide a foundation for the semantic searching of candidate data. The Parser identifies hundreds of different kinds of information within a resume or CV, and clearly tags each data point (for example: first name, last name, street address, city, educational degrees, employers, skill, etc.). The output may be used in HR-XML or JSON format.

The Sovren Job Order Parser extracts relevant data from a job order and contains information to describe job titles, required skills, optional skills, years of experience desired, years of management experience desired, management level, educational requirements, certifications, foreign languages, etc. — 16 categories in all.

Semantic Matching / Artificial Intelligence Matching is a 5th generation profile-to-profile matching engine that works with the Sovren Resume Parser and the Sovren Job Order Parser to provide advanced matching capabilities between candidate resumes and job vacancies. The AIM can parse/extract/classify/characterize and construct profiles of candidates and jobs, and match profiles in any direction: jobs to jobs, jobs to candidates, candidates to jobs, candidates to candidates. The intelligence in the engine adds analytical messages that provide detailed, actionable information about what matched, on what categories, and how the scores were ranked. No other system in the world is as accurate, as customizable, or as informative.

Sovren HR software components are used by recruitment firms and agencies, applicant tracking systems, Human /Resource Information Systems (HRIS), job boards, large corporations, and social networks.

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