Recruiting Agencies
Correspondence Automation
In your company masses of correspondences have to be processed and allocated every day. Each mail must be analysed at great expense before the next step can be taken. It is quite possible to assign certain categories, but the problem is that similar facts are formulated very differently, which apparently makes it impossible to automate this process.
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With semantha® you can automatically classify and assign incoming letters, no matter how they are formulated, regardless of the choice of words.
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With our data extraction, you can easily extract relevant data points from Correspondences and process them further. The combination with semantha’s® natural language understanding is particularly helpful, as it allows you to find and extract data points in specific contexts.
CV Matching
With semantha we have developed an AI tool that overcomes the weaknesses of conventional matching tools. The application does not compare whether certain terms occur in documents, but reads the documents at the level of meaning. And it does so within a few moments.
- Ranking of top candidates at the touch of a button
- Overview of the best internal candidates for any skills
- More objective selection of candidates
- Acceleration of the selection process by up to 50%
- More time for interviews to ensure human fit
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In concrete terms, three scenarios can be presented that come into question for HR departments:
find the perfect candidate for the vacant position:
Find the right person for the job advertised.finding the perfect job for a candidate:
Find the right job for the applicant in your portfolio.keyword search: Search for suitable CVs using keywords.
semantha reads documents at the level of meaning.
These use cases might also interest you…
Knowledge Management
Your company has large masses of unstructured text documents. One of the daily challenges is to search these documents for specific content, whereby normal search functions only offer a simple “keyword search” that does not lead to the desired results. This is particularly due to the fact that, on the one hand, it is not known exactly how a topic was formulated and, on the other hand, the same topics are also formulated quite differently in the various documents.
