AI CV MATCHING

Semantic engine
for candidates pre-screening

Send us your documents, we will analyze the content and return you the professional tags and skills identified

AI CV MATCHING

Semantic engine
for candidates pre-screening

Send us your documents, we will analyze the content and return you the professional tags and skills identified

Thanks to the web server connection, it is only necessary to upload via FTP protocol the candidate’s CV you would like to analyze or the content of the job offer.

The document content is inserted into the semantic engine, which springs into action, enclosing four different actions in just a few seconds:

  • data parsing and extraction;
  • data normalization;
  • semantic analysis;
  • indexing.

Book a free demo

In just 30 minutes, our experts will guide you through our software solution and they will answer all your questions.

  • Introduction to the main features of product
  • Analysis to your priorities

    Book a free demo

    In just 30 minutes, our experts will guide you through our software solution and they will answer all your questions.

    • Introduction to the main features of product
    • Analysis to your priorities

      Thanks to the web server connection, it is only necessary to upload via FTP protocol the candidate’s CV you would like to analyze or the content of the job offer.

      The document content is inserted into the semantic engine, which springs into action, enclosing four different actions in just a few seconds:

      • data parsing and extraction;
      • data normalization;
      • semantic analysis;
      • indexing.

      Data extraction and content categorization

      Once a document is entered, typically a CV (whether it is a reading file, an image, or a scan), the system returns that document in text format (plain text or html).

      In this first step the images are analyzed, thanks to an integrated software of “image processing” based on neural networks, which performs the CV parsing and makes a classification of the text content in sections, such as:

      • Image of the candidate (if any)
      • Candidate CV – with censored personal data
      • Part of the CV referring to: experience, education, personal data, languages and other skills/hobbies/hobby

      Data extraction and content categorization

      Once a document is entered, typically a CV (whether it is a reading file, an image, or a scan), the system returns that document in text format (plain text or html).

      In this first step the images are analyzed, thanks to an integrated software of “image processing” based on neural networks, which performs the CV parsing and makes a classification of the text content in sections, such as:

      • Image of the candidate (if any)
      • Candidate CV – with censored personal data
      • Part of the CV referring to: experience, education, personal data, languages and other skills/hobbies/hobby

      Data normalization and semantic analysis

      The data are normalized in order to improve understanding and a grammar software conducts a logical analysis on the extracted content to identify the semantic meaning of each sentence in the text.

      Our calculation algorithm searches and indexes professional skills (the tasks that uniquely identify a profession) in the analyzed text.

      • Given a text (plain text or html) a title and a language, the system will provide a set of semantic tags linked to the text and its weight.
      • Given a text (plain text or html) or a keyword and a language, the system will provide a clean text to optimize fulltext searches.

      Data normalization and semantic analysis

      The data are normalized in order to improve understanding and a grammar software conducts a logical analysis on the extracted content to identify the semantic meaning of each sentence in the text.

      Our calculation algorithm searches and indexes professional skills (the tasks that uniquely identify a profession) in the analyzed text.

      • Given a text (plain text or html) a title and a language, the system will provide a set of semantic tags linked to the text and its weight.
      • Given a text (plain text or html) or a keyword and a language, the system will provide a clean text to optimize fulltext searches.

      Indexing and matching

      Professional skills are analyzed and evaluated by calculation algorithms, to then be accepted or rejected based on parameters of professional proximity.

      Currently the semantic analysis system is available in 6 languages: Italian, English, French, German, Spanish, Portuguese.

      Indexing and matching

      Professional skills are analyzed and evaluated by calculation algorithms, to then be accepted or rejected based on parameters of professional proximity.

      Currently the semantic analysis system is available in 6 languages: Italian, English, French, German, Spanish, Portuguese.