The recruitment platforms only see the needs of corporate employers. Matchplicity balances the equation to give job seekers more insight and control. The networking model benefits professional associations, individual job seekers, and employers. Matchplicity provides an algorithm-based matchmaking model and brings it to the recruitment space.
The following are some of its key features:
- Access based on roles
- Super Administrator
- A super admin is able to manage organizations, companies, and users
- Client administrators
- The client administrator can manage companies and users.
- Employer administrators
- Employers can manage users associated with their company
- Employer users
- Job seekers
- Job seekers can register and manage their profiles
- Matchmaking algorithms provide users with lists of matched companies and jobs
- An algorithm for matchmaking
- Scraper API
Job seekers and employers were matched according to the different requirements of each employer, which presented major challenges.
It was also challenging to build a software that was scalable. To support the SaaS model, the software also needed to be modified. In order to redistribute the software, the client needed software that could be modified in a minimum amount of time.
The following are some of the key challenges faced by the client:
- Matchmaking between employees and employers using business logic
- Scalability of software
- Easy redistribution of the software
- Implementing a SaaS-based solution
- Data Feed Reconciliation
- Data collection from different sources regarding skills, tools, and job descriptions
Our first step was to understand the matchmaking logic and turn it into code. To get the results the client expected, we implemented the complex matchmaking algorithm.
In order to implement the scalability and SaaS requirements, we used AWS services for Laravel and VueJS.
Daily meetings were held to ensure the implementation met client expectations. Throughout the matchmaking process, data results were discussed with clients on a daily basis and improved based on their feedback.
Our solution is summarized below.
- Nginx server hosted by AWS Services
- Databases on RDS
- Multi-tenancy for SaaS and APIs with Laravel
- Implementation of the front end using VueJS
- Scrapper APIs with Python Flask
- Match-making algorithm implementation by domain experts.
Using a Scraper API, we reconciled the Data Feed. From different sources, the Scrapper API collects data such as skills, tools, and job descriptions.
Matchmaking between job seekers and employers was successful. Posts can be liked, commented on, and shared in the activity feed. Job postings can include unique elements like location, skill requirements, etc. Applicants are matched and shown jobs based on their qualifications.
A SaaS structure was successfully implemented based on the client’s expectations. For a couple of years, we maintained software.
More Related Case Study
We provide tips and advice on delivering excellent customer service, engaging your customers, and building a customer-centric business.
You Might Have Questions
We are here for you!