Emply.ru is looking for an investor

A week ago, our investor said that for a number of reasons his income had declined and he could no longer finance us in full. C Augustus team will have to be reduced. To continue to develop and promote, we are looking for additional investments.

During the work we have done two independent online projects, each of which has no analogues in our market:

• Emply.ru Job Search Engineer
• Service scoring summary emply.ru

They are unique due to the use of our technology to extract facts from vacancies and resumes. Both services differ from existing solutions on the market in that they allow to operate not with full-text search categories (the text contains “programmer” or “developer by” or “developer”), but with domain categories (programmer vacancy).

Such an approach allowed us to implement resume scoring - evaluation of compliance with the vacancy parameters. This service for an employer or recruitment agency allows you to save recruiter time during the initial selection of a resume.
The user is required to set vacancy parameters (mandatory requirements and evaluation parameters, which will be scored) and choose which resumes to analyze. You can upload resumes for analysis yourself (we made an anonymizer program for protecting personal data, which leaves the contacts on the employer's machine, and uploads anonymous resumes on emply.ru) or analyze resumes from our database, which is updated with spiders (just 141,636) summary). As a result, the user sees a list of the most suitable candidates for the vacancy. For each indicated a compliance score (in percent).
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For text analysis, we created a system that we modestly call a “parser”. The parser allows you to analyze job texts and resumes and not just break them into blocks, as other resume parsers do, but extract facts from them (positions, skills, industries, etc.)

To make it all work, we created a knowledge base of the subject area containing directories of these very facts, their possible names (how this object can be called in a resume / vacancy), links and hierarchies of objects, and about a hundred thousand manually processed job texts and summaries, which our ML algorithms are trained.

All this wealth is documented, covered with tests, CI mechanisms are established, automatic deployment. And even the team until August, all in place.
We have a preliminary agreement on the use of scoring with many companies, and we are negotiating integration with the largest Russian ATS (Applicant Tracking System) and job sites.

We welcome any suggestions!