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It still happens that LinkedIn recommends the wrong job. Like the painter was pointed to a job as a hairdresser because both professions work with paint. The use of AI can prevent this kind of misinterpretation, promises
Kok has a background in artificial intelligence and a PhD in linguistics. For the past five years at the Textkernel company, he has been working on parsing and matching technology for recruiters and HR.
Textkernel was founded in 2001. In early years, it set the tone with solutions such as Resume & Jobs Parsers and Labor Market Insights. That was later joined by AI-driven recruitment technology, with deep learning and LLM (Large Language Model) parsing, semantic search and matching.
Companies that search for talent based on skills often find candidates faster than those that only look at resumes, Kok said. Using matching technology, for example, you can also find candidates who can be retrained as data scientists.
Navigating the landscape of job-related data can often feel like a journey through chaos. To address this challenge, Textkernel presents Skills Intelligence, a collection of APIs that enables streamlined data standardization and automation Each skill and occupation is linked to a variety of synonyms, acronyms and alternative wording.
Randstad Germany, a leading provider of HR and staffing solutions, has partnered with Textkernel to implement its AI-driven source and matching technology. Randstad can now effectively search multiple sources, including resumes, social media profiles and job boards, to identify suitable candidates and match them with open positions.
And
According to Kok, though, many companies still lack insight into their own talent pool due to a lack of data. You would have to aggregate profiles of skills in a database at every company.
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