How This Bengaluru Based Startup Is Using AI To Optimise The Entire Value Chain Of Recruitment Process
The recruitment industry in India is growing at 21% per year and is approximately worth ₹35,000 crores as per Executive Recruiters Association and Ernst & Young. Currently, traditional HR management software is being used by enterprises. Even though these solutions promise end-to-end management of the recruiting process, there are gaping holes that need to be filled. Solutions that use cutting-edge technology along with the power of AI to integrate with these existing platforms can solve some real pain points of the industry. With a passion for transforming the recruiting process with AI, California-based startup Skillate is leveraging deep NLP algorithms and Optical Character Recognition (OCR) techniques to enable the entire recruitment experience seamlessly. Recently, the company raised 1 Million as Pre-Series-A funding. Currently, the company has offices in San Francisco and Bengaluru. For the weekly column, Analytics India Magazine interviewed Anand Baranwal, Co-founder at Skillate to gain more insights on how the company is leveraging AI and NLP for seamless recruitment experience. Founded by Anand Baranwal, Kumar Sambhav and Bipul Vaibhav in 2016, Skillate is an advanced decision-making engine to make hiring easy, fast, and transparent. The AI-based platform in Skillate helps in optimising the entire value chain of recruitment, beginning from creating the job requisition, to resume matching and candidate engagement. Flagship Product The flagship product is Skillate’s Matching Engine, which works by mapping all the relevant profiles with the job requirements from skills, education to experience and recommends the best candidate. The product is integrated with all the external channels, and ATS to source resumes directly. Trained with over 20 million diverse profiles, Skillate’s AI algorithm helps to screen and shortlist resumes with just a click. The proprietary tools and products of Skillate include: ● Automated Resume Parsing: An AI and deep learning-based model trained on 120 Million profiles to understand the semantics of each and every statement present in the resume. ● Job v/s Resume Matching: This is done by persona matching rather than keyword matching. The recommendations are based on industry, desired skills, roles, and responsibilities to get the best candidates in a click. ● Smart Chatbots: Smart chatbots are used to automate the entire pre-screening conversation with the help of a bot. ● Job Description Assistant: Job description assistant is used to help enterprises in attracting great talent and positioning themselves for employer branding. How This Product is Different From Other Products In The Market? To this question, Anand replied that Skillate’s AI algorithms have been trained with a dataset of 20 million profiles. Its understands the hiring pattern of companies and recommends candidates as per its learnings. The algorithms not only automates the recruitment efforts but does it intelligently and smartly. The founders believe 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 recruiters’ actions, job descriptions and resumes inflow to improve the recommendations for recruiters. How Skillate is Using AI and ML Skillate created an entirely new classification system to segregate the resumes into different types, based on their template, and tackle each type differently. Resumes that contain tables, partitions, etc. required higher-order intelligence from the software. For such complex types, the company uses Optical Character Recognition (OCR) along with Deep natural language processing (NLP) algorithms to extract the text from resumes. To deliver a robust classification algorithm to precisely segregate the resumes, the founders amalgamated different technologies to develop a highly accurate and fast text extraction method. The algorithm also incorporates the power of self-learning, where it learns a company’s hiring patterns to enhance the results. Core Tech Stack Skillate’s core technology stack collects resumes from all potential sources such as internal database, ATS, Google Drive, mailbox, referrals, external agencies, and career pages. It then runs its ML model on the incoming resumes. The company provides several tools around this technology to enable recruiters to quickly make decisions and push forward in the hiring pipeline. Skillate works efficiently at scale, and the platform has the ability to process 5 Lakh resumes screening in 4 seconds. Challenges Faced According to Anand, convincing the team heads that technology can make their work easier and more efficient was a challenge. Anand said, “Even after our solutions’ convinced them, adoption in the company remained an issue. Our dashboard’s highly intuitive design, along with our committed customer success team, ensured that adopting the Skillate platform was a smooth process with little training to the recruitment team.” Roadmap Starting from 2020, the founders are looking forward to a future full of growth and new market penetration. Anand said, “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.”
The recruitment industry in India is growing at 21% per year and is approximately worth ₹35,000 crores as per Executive Recruiters Association and Ernst & Young. Currently, traditional HR management software is being used by enterprises. Even though these solutions promise end-to-end management of the recruiting process, there are gaping holes that need to be […]
With a passion for transforming the recruiting process with AI, California-based startup Skillate is leveraging deep NLP algorithms
["Startups"]
["Go", "AI", "chatbots", "ML", "Scala", "RAG", "NLP", "deep learning", "analytics", "Startups", "R"]
["AI", "ML", "deep learning", "NLP", "analytics", "RAG", "chatbots", "R", "Go", "Scala"]
A Technical Journalist who loves writing about Machine Learning and Artificial Intelligence. A lover of music, writing and learning something out of the box.