https://store-images.s-microsoft.com/image/apps.22946.25028215-b09f-4540-9463-5a69a856b559.7a0a9604-3b41-4ec1-a568-de1329fd74da.ff33f999-526f-402c-bd51-d293259216bb

CV and Job offer parsing

Autor: Xtramile

parsing technology, analyze a CV, analyze a job offer

Our parsing technology would help to analyze information from CVs or job offers. Thanks to a combination of proprietary computer vision models and embedding NLP, it can accurately and properly extract information from even complex layouts (CV in columns, Compacts CVs, etc) or formats.

What are the main functionalities of our tool?
  • The parsing technology can analyse CVs and jobs offers in any type of format (image, word, pdf, etc);
  • It operates in all the major languages (English, French, German, Dutch, etc);
  • It enriches and standardize the data;
  • It offers the possibility for the recruiter to anonymize information as needed.
  • It can be deployed on your existing CV & job offers database regardless of its size and/or real-time on your website

Key benefits
-It helps to reduce the costs of manual entries: you can save up to 70-80% of time through this automation;
- It enhances the candidate experience on your website and limits the information that are required: You can increase your applications by 30-40%;
- A recruiter can use more accurate information to search through the CV database: Increase the accuracy of the matching process and bring operational efficiency to your recruiters.

How can you use it?
- Integrate our parsing technology with your ATS to analyse your documents as part of the recruiter's journey;
- Integrate our technology to your website to fasten the application process: the candidate would only have to upload his CV and no need to fill a form, our technology can take care that!

Rychlý přehled

https://store-images.s-microsoft.com/image/apps.13514.25028215-b09f-4540-9463-5a69a856b559.7a0a9604-3b41-4ec1-a568-de1329fd74da.ef94d31b-4aab-417a-bd82-a68b8ba24940
https://store-images.s-microsoft.com/image/apps.45611.25028215-b09f-4540-9463-5a69a856b559.7a0a9604-3b41-4ec1-a568-de1329fd74da.63ff6e54-0601-45d0-acd9-d4b556c1e3dd