Online Hiring Platform Bevy Grabs Fresh Funding From Varun Agarwal And Others

Bevy, an online hiring platform which helps job seekers secure interviews with recruiters via its Smart Placement Process, has raised an undisclosed amount in funding, from a couple of angel investors, which includes Varun Agarwal of Alma Mater and other angels.




Bevy, similar to other hiring platforms in principle, however differentiates itself from the rest largely through its clean, intuitive design and more user friendly interface.



The biggest issue, which job seekers in India today encounter on various hiring platforms, isnt lack of jobs or un-scheduled interviews. It is actually the out-dated, traditional and hence complicated user-interface with these platforms which is more of a pain to go through than actual interviews.



Bevy on the other hand, has a simple, unique and clean UI, based on modern web tech, and built mostly on the more popular flat UI concept. The interface is minimalistic, easy to use, and makes the entire process of getting to a recruiter via its platform, a breeze.



Moreover, Bevy is also looking to fill in that huge time lag (usually in weeks) between when a company decides to hire someone and when the company actually starts the process of that particular hiring.



Luke Walton, Founder & CEO says,



When looking to solve the many problems associated with hiring, we thought it only made sense to start at the beginning, the introduction, the first time a candidate speaks with a recruiter.


To start searching for jobs on Bevy, you initially need to create a profile. Post creation, Bevy automatically matches you to relevant recruiters via numerous scoring metrics which finally deliver you a profile score. Once matched, you can apply for relevant jobs and get to recruiters.



As of now, the company is using its freshly raised capital for hiring experts in the fields of HR, Artificial Intelligence, and Software Development. It is also hiring data scientists from IITs to make its platform even more self-reliant in itself, in a sense that it matches users with more relevant jobs.


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