Most recently I was invited to moderate a panel discussion related to time-to-fill.
Essentially the premise on how do you accelerate the process from application to the onboarding of the candidate.
Traditionally, this would mean getting the applications in, manual screening of those applications, going through rounds of selections (which include phone/face-to-face interview), offer and onboarding.
End to end, this process can take months.
And this isn’t something that is unique to traditional businesses.
My cousin personally went through 6 rounds of interviews at Facebook over 3 months (the longest I’ve heard is 7 rounds over 9 months with Standard Chartered).
That wasn’t the worst of it. In the final round, he was told that he is too experienced for the role.
Yes, something they could detect at the point of application requires x months and hours of discussion to realize.
Better Screening Perhaps?
Could my cousin not go through that awful candidate experience if better screening mechanism was in place?
Most employers begin their application process by fronting them with an Applicant Tracking System.
An applicant tracking system (ATS) is a software application that enables the electronic handling of recruitment needs.
In Singapore alone, there are at least 20+ ATS providers.
But not all are built the same way.
Unique features aside, the key manner that ATS can help to make recruitment better is to automatically parse the unstructured document that we called a resume.
And ensure the parsed data could be stack rank.
This way, the recruiter need not spend an unnecessary amount of time and treat the 100 applications as equal.
Instead, she will depend on the algorithm and just focus her attention on the top 10% that scores higher than the rest.
Ensuring stack rank can happen
But this doesn’t come about just because you are already on an ATS.
Some ATS may not have any parsing/ranking feature or they might have their own basic version which is not sophisticated enough to handle all types of unstructured data.
For example, their only means of matching and comparing is purely based on keywords and nothing on semantic.
This would mean that the resume of someone who is the Secretary to the Managing Director would match well with the job posting for a Managing Director.
Something that RChilli has been solving since 2010.
Who is RChilli?
RChilli is a specialist in providing parsing, matching and enrichment to every recruitment management system.
They not only make sure that resume data is captured, managed and analyzed in the most proficient manner but also facilitate quick talent acquisition with the help of resume parsing, semantic search & match and resume enrichment.
RChilli technology would be able to automatically extract data from any resume types and use those to fill up relevant forms so that data points can be more accurate.
Going beyond keywords, RChilli semantic match technology factors in nuances and help reduce false positives (refer to the example mentioned earlier).
And it is not just a Jobs to Resume matching only. You can also do Resume-Resume, Resume to Jobs and Jobs to Jobs.
Beyond what is provided on the resume, RChilli would match the unique identification key of the resume against what it can find on the internet.
This will allow the profile to be enriched with more and updated data from social profiles.
How RChilli helped impress.ai
impress.ai is an AI chatbot platform for recruiters.
Their conversational bots conduct competency-based structured interviews using techniques from Industrial-Organizational Psychology, specifically situational judgment questions.
These chatbots autonomously interview, engage, and shortlist candidates at scale, 24/7, and actively fight human bias by hiding biasing information from human reviewers.
The main challenge impress .ai faced was that they were not able to build anything with a resume in PDF format.
Also, a simple parser only gave them an email and name.
Using the right technology to enhance the productivity of its chatbots was a major concern.
impress .ai needed a solution which could put an end to this trouble.
They checked the market and found RChilli which suited their requirements the best.
The reason they chose RChilli was its easy to use API and a strong reputation in the HR Tech industry.
This allowed impress.ai to build smarter resumes which help them ask contextual questions in interviews to candidates through their chatbot platform.
With RChilli, they achieved 80% interview completion rate overnight.
“RChilli is very easy to use and implement. Also, their customer service is responsive whenever we have issues. Their support team is our go-to and there isn’t a problem they cannot solve.”
Dr. Vaisagh Vishwanathan, Co-founder and CTO, impress.ai
Question: Keen to learn more about RChilli? You can also leave a comment below.