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Automated Job Portal Enhancement with Intelligent Categorization and Application Workflow

dac.digital

Human Resources

Key Challenges Identified

Inaccurate job listing categorization causing mismatched candidate-job pairings, manual application processes creating operational bottlenecks, lack of automation in keyword extraction, and insufficient technological strategy hindering platform scalability.

About the Client

A Stockholm-based startup specializing in AI-driven recruitment solutions and unbiased hiring tools

Strategic Project Goals

Implement automated job categorization with 95%+ accuracy
Develop intelligent application workflow automation
Establish scalable infrastructure for platform growth
Enhance user experience through intelligent data processing

Core System Functionalities

AI-powered web scraping for job listing analysis
Automated form field population with contextual understanding
Machine learning-based job-candidate recommendation engine
Dynamic categorization system with keyword mapping
Real-time relationship mapping between job offers and candidates

Technology Stack Requirements

Python

Selenium

FastAPI

Neo4J

PostgreSQL

Scikit-learn (Sklearn)

System Integration Needs

Third-party job portals API
Candidate database systems
HR management platforms

Operational Requirements

Horizontal scalability for 10x user growth
Real-time processing with <2s response time
Enterprise-grade data security (GDPR compliant)
High-availability architecture (99.9% uptime)

Expected Business Transformation

Enables rapid team formation (16-day implementation cycle), improves job matching accuracy by 40%, reduces application processing time by 70%, and establishes foundation for AI-driven recruitment innovation while maintaining compliance with Swedish data protection standards.