An AI-based recruitment platform, Skillate, helps the recruitment team find the right candidate by using their career page and providing database to make the process faster and transparent. The chatbot developed by Skillate looks after the first round of the interview with candidates that makes it easier to shortlist them for the next round.
The Co-founder of Skillate, Anand Baranwal gets into an exclusive conversation with Radhika Udas from Express Computer. Anand Baranwal is responsible for the Business Development, Marketing, and Hiring for Skillate.
What is Skillate and what problem does it solve in the current recruitment process for HRs?
Skillate is an AI recruitment platform, making recruitment easy, fast, and transparent. The product helps in optimizing the entire value chain of recruitment, beginning from creating the job requisition, to resume matching, to candidate engagement.
The Skillate platform helps enterprises in AI-powered job posting, job candidate matching, candidate screening conversations with the help of chatbot, automatic interview scheduling, and onboarding a candidate. Its product can be used standalone or can be integrated with existing enterprise recruitment software.
Skillate reduces the screening time by 93 percent and brings in a 2X increase in the candidate’s conversion rate. The chatbot also helps keep internal databases updated; this, in turn, reduces cost. It can also make your existing Applicant tracking system (SuccessFactors, Taleo, etc.) smarter by the smooth API integration without ATS replacement and hassles.
What is the exclusiveness of your startup, that’s unique from other gigantic players in the market?
Skillate’s AI algorithms have been trained with a dataset of 20 million profiles. Its understanding of the hiring pattern of the companies and the recommendation of candidates as per its learnings are very unique in nature. It not only automates the recruitment efforts but does it intelligently and smartly. The startup believes that every company, especially the ones in India, have different hiring patterns, not just in verticals but also based on location. Skillate’s matching algorithm is made to self-learn these patterns based on recruiter actions, job descriptions and resumes inflow to improve the recommendations for recruiters.
Over the past few years, with the power of AI, Skillate has already processed 3.5 million resumes, and this continues to increase at the rate of 10% every month. Skillate’s SaaS product is industry agnostic and currently has clients including Yes Bank, OYO Rooms, L&T Construction, L&T Financial Services, Saint-Gobain, Bigbasket, and others.
What technologies is Skillate using? How is AI helping transform recruitment?
Even though there is a multitude of facets where we use AI to explain it in detail, let’s take the example of Resume Parsing, a problem that has existed for years and continues to haunt recruiters.
Resume Parsing, formally speaking, is the conversion of a free-form CV/resume document into structured information — suitable for storage, reporting, and manipulation by a computer.
The problem of Resume Parsing can be broken into two major subproblems — 1. Text Extraction, and 2. Information Extraction. For building a State-of-the-art resume parser, both these problems need to be solved with the highest possible accuracy.
We explored several libraries to extract text from pdf, doc, Docx, etc. type of documents, but none of them could provide the quality of results we were aiming to reach. It became evident that text extraction could not be solved by a single type of algorithm alone.
So we first created an entirely new classification system to segregate the resumes into different types, based on their template, and tackle each type differently. Some of the types were straightforward, but most of them (like the ones that contain tables, partitions, etc.) required higher-order intelligence from the software. For such complex types, we decided to use Optical Character Recognition (OCR) along with some Deep NLP algorithms on top, to extract text.
For every problem, there is a hard way and a smart way to solve it, and we decided to go with the latter. OCR is a very generic problem which has been researched upon and solved by the biggest tech companies in the world. The best part is that this technology has been open-sourced as well! Therefore, in this context, the hard way would be to build a deep learning model from scratch for OCR and NLP, and the smart way was to use the power of open source and deploy an off the shelf model for the task.
With the help of our classification algorithm to segregate the resumes, we were able to amalgamate different technologies and obtain the best of everything, to build a highly accurate and fast text extraction method.
What does the future look like for AI in the HR domain?
Companies today need to understand that contemporary recruitment is a two-way street. The candidate is gauging the potential of exposure and career growth just as much as the recruiter is gauging the candidate’s skill and experience. With a tight job market and a demand for quality candidates, recruiters need to up their game to win in this ‘war of talent’.
Even in today’s world, you find fuzzy job descriptions and application forms that take hours to fill. Let’s face it – poor candidate experience will never attract the right talent. Companies need to invest in AI-based technology that can not improve the candidate experience but make the whole process a lot faster. Automated JD assistant and smart career pages are just a few examples of similar technologies. Platforms that can extract information beyond resumes (intent to relocate, work on night shifts, etc.) without involving manual labor can be another game-changer in hiring quality candidates. Generally speaking, organizations need to put themselves in candidate’s shoes and match the tech-savviness of their potential candidates if they want to come across a great employer.
Coming to the financial perspective, are you a bootstrapped venture? If not, kindly elucidate the nature and amount of funding raised.
We raised $1 mn in October 2019 in our Pre-Series-A funding round.
What are your immediate and long-term milestones like?
We look forward to a future full of growth and new market penetration. We are focusing on international expansion as a key focus area, especially in the US. As we expand operations, we are making our product multi-lingual and more scalable so that it can handle even larger volumes.
Lastly, any word of advice for budding entrepreneurs?
Entrepreneurs need to pay attention to sales, even when their products are in a developing stage. Focus more on execution than planning.