To make the recruitment process more efficient: Internal CV databases are large (10k+ CVs). Recruiters lack the time and attention span to go through thousands of CVs to find the best match for a role.
To remove the selection bias, more noticeable in the earlier stages of the recruitment process.
To eliminate the need for complex searches and the lengthy process of rifling through filters to find perfect candidates.
Used advanced Machine Learning and Natural Language Processing to train the bot to create rich candidate profiles.
Used text mining and pattern discovery to learn recruiter preferences for ideal candidates, and installed a Recommendation Engine to suggest the top candidates for a specific job search in seconds.
Implemented entity extraction and intent recognition techniques to develop the chat interface.
The unsupervised model resulted in a continuous decrease in time spent on shortlisting candidates.
The conversational interface enabled plain-spoken, human responses when interacting with recruiters.