Automation of Job Data Migration Using AI-Powered Web Application

Non-profit

Education

Identifying Inefficiencies in Manual Job Data Aggregation Processes

The organization relies heavily on manual labor to aggregate and process job listings from various partners and internet sources, leading to time-consuming, costly, and error-prone workflows that hinder growth and operational efficiency.

About the Client

A non-profit organization dedicated to supporting first-generation college graduates by aggregating and publishing job listings from multiple sources.

Goals for Improving Job Listing Processing Efficiency

Reduce the time required to process a single job listing by at least 66%, decreasing from approximately 15 minutes to 5 minutes per listing.
Automate the data migration process from external URLs to minimize manual input and reduce operational costs.
Enhance accuracy and consistency in job data aggregation to improve platform reliability.
Develop a scalable and secure web application that supports high-volume processing and future growth.

Core Functionalities for Automated Job Listing Migration System

User input interface for submitting job offer URLs.
Automated extraction and processing of job data via AI models.
Integration with external and internal job listing systems for migration.
Secure handling of user data and job information.
Logging and error handling to ensure data integrity.
Scalable architecture supporting increased processing volume.

Preferred Technologies and Platform Architecture

Cloud-based infrastructure with scalable compute resources

AI models for data extraction and processing

Web application frameworks supporting fast development and deployment

Secure APIs for integration with external and internal systems

External and Internal System Integrations Needed

Partner systems for data migration and synchronization
External job listing sources for data extraction via provided URLs
Internal databases or platforms for job listing storage and management

Non-Functional System Requirements

System scalability to handle increased processing volume with minimal latency.
Performance optimization to ensure processing times are reduced to within 5 minutes per listing.
High levels of security and data privacy compliance.
System resilience and fault tolerance to prevent data loss or downtime.

Projected Business Benefits and Efficiency Gains

Implementation of this AI-powered migration system is expected to reduce job listing processing time by approximately 66%, significantly lowering operational costs, minimizing manual effort, and increasing overall platform reliability and scalability, thereby enabling the organization to handle higher volumes of job data and support growth initiatives.