Reading people is sometimes a talent that is cultivated over years of working with them and for some lucky recruiters a knack that they’re just born with. In the field of recruitment, it is both an art and science, knowing if the candidate that you have picked is actually fit, and also, does he/she really want to fit and stay.
After several years in business, a set of experienced recruitment professionals with over 75 years of combined talent acquisition experience in the field noticed that organizations were too dependent on only the recruiter’s ability to read people. Organisations who had a talented recruiter were in a good place but, if they didn’t or their prized recruiter left, they had no way of ensuring success with candidate.
Enter Bruhat. Bruhat is an AIHR (Artificial Intelligence in HR) company that utilizes artificial intelligence to make it easier for hiring corporates to not only effectively manage their people requirements, but also obtain actionable insights, to drive productivity and engagement. The recruiting process also ushers in a new level of transparency never seen before in talent acquisition consulting.
Bruhat is an AIHR (Artificial Intelligence in HR) company that utilizes artificial intelligence to make it easier for hiring corporates to not only effectively manage their people requirements, but also obtain actionable insights, to drive productivity and engagement.
Today, we have unfettered access to nearly 100% of the information that a recruiter gathers about the potential candidate during the profiling life-cycle, thereby increasing the probability of offer acceptance and joining. No job site or social media site, is able to capture the extent of information that Bruhat’s AI tool processes. Using Bayesian inference, the tool creates the relative offer acceptance scores of a set of shortlisted candidates, and determines which candidate has the highest probability of acceptance. Information such as competency scoring, identity cloud capture, keyword cloud capture and prior offer acceptance patterns are analysed with individual weights and priorities to ensure interview optimization by the hiring company.
Yes and no. In the 2000s, following liberalization in India, booming industries started hiring aggressively across the board. Talent managers interviewed several hundred candidates in the span of a few months but found that candidates were refusing the job offers. Even if they did accept they would soon be on their way out. The same old hiring processes were no longer relevant.
Bruhat’s founders discovered that the fault lay in the profiling systems used by recruiters. Investing in a robust data capturing mechanism that parsed data from hard-copy resumes (those were the days when electronic versions were lesser known), while also adding extensive profiling information captured in a “Dialogue Box” during the recruiter’s conversation with the candidate, the team was able to leverage both structured and unstructured data that emerged during candidate interactions.
That was the beginning, with a manual profiling process that hand-picked candidates through extensive validation methods. When the methodology was found to deliver very high success rates (90% of all second career women accepted the offers and joined the hiring companies), the process was then scaled up using extensive computerization in order to interpret many different unstructured data streams such as offer acceptance ratios, identity stages, keywords associated with success rates, et al, in order to effectively predict results, improve accuracy and optimally match the job-seeking candidate and the hiring organization.
Using statistical tools such as perceptual mapping and canonical correlation, AVTAR was able to provide candidates who were shortlisted using methods other than the bland plug-and-play.
Bruhat, today uses Artificial Intelligence to predict offer acceptance ratios of every candidate so that recruiters everywhere can finally