Unleashing AI in Recruitment - How AI changed traditional approach in Talent Acquisition
Experties: Custom Web Development Job & Recruitment Management Human Resource Management (HRIS)
Verticals: Utilities Consulting
Technologies: Node.jsPythonMongoDBExpress.JSjQueryAngular Stripe Payment Gateway
Client is one of the largest consulting company in utilities sector, in the business of driving strategies for future growth and helping utilities companies with transformation in the area of productivity and operations.
Time Duration 4 Months
Team Effort 3 Persons
Business NeedsClient was receiving thousands of applications per month spread across many roles and wanted to utilize AI based approach so that talent acquisition team can engage only with candidates who are the best fit for specific positions amidst the challenges of availability of skills and cultural fitment. Client was looking to use AI (Artificial Intelligence) for the below specific requirements:
- Manual and transactional tasks such as Resume collection, candidate matching, prescreening assessments and interview scheduling- Improving the efficiency of the recruitment cycle while maintaining the fine balance between speed and quality.
- Tracking the Talent acquisition metrics- Quality of Hire, Time to Hire, time to fill
- How Talent Acquisition can focus more on- Building workforce for future.
- Brand positioning through enhancing candidate experience- Specifically for candidates who were not applying to the right positions at given time and were missing opportunity to work in the organization.
Strategy & SolutionOne main question that client needed to think was whether they should buy the AI application for HR or they should get the AI applications build for their own usage. Since client was not having deep experience in AI, hence taking into consideration Flexsin strong competency in building similar solution, Flexsin was asked to build the AI application from scratch.
- Client main objective was to create a candidate enhancing experience that starts involving job seekers from the first interaction itself, while in parallel developing understanding of their suitability for roles that match their competency.
- Previously, jobseekers would meet for the first time at the job interview. By leveraging AI, jobseekers can now have real-time interaction via a chatbot, leading to more customized application process for job seekers. Job seekers with good amount of information of vacant positions in turn leads to a stronger fit of job applicants for roles.
- Videos got integrated into the process to give a much more information on hand to job seekers what it is like to work at the organization. Candidates were able to record video interviews from application's UI or their browser of choice and submit them for review
- High-level data engineering and data science capabilities were used for designing, developing, and deploying an AI module. This intelligent module had built-in capabilities of processing millions of CVs and scoring them according to the data available.
- Module was programed for comparing it with the job description the talent manager was looking for. Moreover, as most of the CVs may not be up to date, the application was designed to fetch the latest information about a specific candidate by connecting with a host of third-party apps. These third-party applications were the ones that collected the latest information and kept feeding the database.
- Managers can review more accurate job matches, job seekers can explore job openings relevant to their skills, and recruiters can find qualified applicants without spending time on manual research and job-to-applicant matching. Talent manager were able to schedule a video interview with any candidate and quickly view the social media profiles for every candidate
Node.js | Python| MongoDB |Express.JS |jQuery |Angular | Stripe Payment Gateway
Flexsin's team designed a user interface that had the latest component of Angular in it. The front-end developers kept in mind the importance of building a simple and responsive UI.
Harnessing the power of Node and MongoDB, the backend on the website was fast and highly available for serving the user request as optimally as possible. Using the Shading concept of MongoDB for making sure the database queries were processed fast. Node clustering was leveraged for making the application highly available with zero downtime
Below steps were taken for the development of AI based application
Defining the use case and discussion with business stakeholders on MVP ïƒ checking the availability of Dataïƒ carrying basic data explorationïƒ defining model-building methodologyïƒ defining model validation methodologyïƒ automation and production roll out.
- This was a four month project
- Weekly discussions were held between Flexsin team of data enginers, data scientists & talent acquisition team from client side.
- There were two in-person visits to client site for the objective of extensive process review and security considerations.
- Features were delivered sprint wise as per scrum methodology.
Since Flexsin also had a maintenance contract and hence discussion with the management on the continuous monitoring was done as once AI application has been published and deployed for use, it must be continuously monitored as, by understanding its validity. Proper training was organized from time to time to remain mindful of what process must be followed to ensure the model is kept up to date.
Data Provided to Flexsin:
- 20,000 job seekers resume in raw format
- 30 job descriptions from production department
- 10,000+ historical hiring decision points
I wanted to build an AI-enabled app that could reconfigure the future of the talent industry. And wi..
San Jose, CA
San Jose, CA
- Integrating AI into the overarching corporate culture for turning disruption into digital transformation
- Iterative improvements were made to the model with training decisions as well as with qualitative feedback from Talent acquisition team members. Studies by the client revealed that the Flexsin model identified between 15 and 20 times as many top-quality candidates for a given role relative to their existing candidate database search process.
- For a position - "Production Design Engineer", client found 325 candidates from a data sample of 10,000 using their existing process. 219 of these were deemed not to be a good match, and of the remaining only 40 were suitable for interview. The Flexsin model found 700 candidates from the same sample, of which 561 were suitable for interview.
- In a head-to-head benchmarking test, the new automated process took a few minutes for the outcome compared to 10 hours for an employee.
- Previously, the client's email open rate was an industry average of 3-9%. After implementing the AI platform, the open rate for candidates for potential job jumped to 40%-60%, with a 75% apply rate.
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