CalledIn

CalledIn is a job matching program meant to connect employers and job seekers in areas without internet & or access to job boards like LinkedIn, Glassdoor, etc.

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CalledIn

Inspiration

We wanted to provide a platform for people who have restricted access to the internet or difficulty navigating it and provide them with the opportunity to connect with employers. This project also creates a greater diversity in job outlook by providing various jobs ranging from construction workers to software engineers. The program also aims to combat the shortage of labor.

What it does

Applicant Side

  • Has an automated voice calling system through Twilio
  • Take in the voice data and transcribe it
  • Matches the skills of the applicant to job posts from corporates
  • Sends an SMS/Voice call message with information about the Job opportunities

Employer Side

  • Collaborate with companies to create a job database (through Twilio)
  • Matches skills of an applicant pool with job postings

How we built it

Prerequisites

  • Have a public hosting site available
  • Create a Google Firebase account
  • Create a Twilio account
  • Download the following libraries:

pip install nltk // the natural language processing module that was used to clean the text data and obtain necessary keywords

pip install firebase-admin // hosts the firebase

pip install speechRecognition // used to convert audio files obtained from the Twilio server into a text file for language processing

Twilio hosted on AWS Server

Twillio was used to create an automated calling system that interacts with the job applicants and the companies/job listings. The backend server is hosted on AWS.

Firestore Database

Firestore is a real-time NoSQL Database that stores job applicants and listings. It queries based on age, proximity to the job, and qualifications.

Usage

CalledIn is a job matching program meant to connect employers and job seekers in areas without internet & or access to job boards like LinkedIn, Glassdoor, etc

Challenges we ran into

  • Backend Server connections
  • Switching between different languages for common functions
  • Accuracy vs. complexity of the speech recognition module

Accomplishments that we're proud of

  • Were able to deliver the MVP
  • Made use of complex frameworks, servers, and databases.

What we learned

  • Everyone in the team learned time and project management and new languages and frameworks.
  • We also learned new tools like the Firebase and Twilio web services

What's next for CalledIn

  • We plan to increase the accuracy of our speech recognition model
  • We also want to implement google translate into the project to allow users to use a language comfortable for them
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