Automated shortlisting
Schedule a demoAutomated shortlisting works with recruiters to save time
Automation isn’t here to replace recruiters. Automating more workflow steps gives back precious time to your recruiters to build personal relationships with candidates.
Shortlisting can be a labor-intensive, time-consuming process. But this can cost you not only resources, but placements when candidates are placed faster by someone else. AI matching can help recruiters generate shortlists, but recruiters should always be in control and decide which candidates are best based on their knowledge and experience.
Step 1: Automated shortlisting isn’t about replacing recruiters
Automated shortlisting is about helping recruiters find top talent quicker, not replacing them. As with any industry that has embraced automation, blending “machine intelligence” with “human intelligence” is key.
Automated shortlisting matches your job description to candidates based on the entire vacancy, rather than just keywords or phrases. This way many (if not all) synonyms and related terms are added to the query to bridge the language gap between how hiring managers write job descriptions and how candidates write CV/resumes. This leaves you with a broad candidate shortlist, which you can then narrow based on your knowledge and experience in step 2.
Step 2: Recruiters in control to make the difficult decisions
With an automated shortlist generated, recruiters can then tackle the difficult job of assigning importance to the various parts of the search, and discover the nuance of the position from both the hiring manager and potential candidates. This way, recruiters save hours of shortlisting candidate supply and can focus on finding the best fit from a list of good matches.
Automated shortlisting has the primary goal of saving recruiters time, with bonus points for finding candidates recruiters may have missed. But “recruiters” is still the most important word in that sentence.
Matching CV/resumes to jobs
Textkernel can directly match a profile (from your CV/resume database, job board or social media) to both your own jobs and all online jobs in the Netherlands, Germany and France with our product Jobfeed.
Textkernel uses the text of the CV/resume of profile of the candidate and automatically creates a search. The search can be performed on your jobs and with on all online jobs found by Jobfeed, Textkernel’s Big Data tool for jobs. Jobfeed collects and structures all online jobs and makes them searchable. With the addition of Jobfeed to Match!, you can match a profile to all online jobs in the market, allowing you to quickly find that relevant job for our candidate, client or employee.
Use case: help your client or employee to a job
When an employee becomes redundant or you get a new client for mobility, reintegration or outplacement, you can use their profile to find them a new job with Match!. With the addition of Jobfeed you get access to all online jobs in the Netherlands, Germany and France that you can easily and accurately search.
Use case: show fitting vacancies to job seekers
With Textkernel’s technology you can easily offer job seekers and applicants an overview of your jobs that match his or her CV/resume. When you integrate the semantic search technology into your job board, career site or website, you can offer job seekers the option to easily search your jobs with the user-friendly interface of Textkernel.
