PROJECT OVERVIEW
Abstract
This case study presents the design of a Job Search Intelligence Platform that automates and optimizes the end-to-end job application process using AI and workflow automation. The platform integrates resume and cover letter generation, job discovery, application tracking, recruiter intelligence, interview preparation, salary analysis, and career analytics into a single system.
Unlike traditional job portals that focus solely on job listings, this platform acts as an intelligent agent that prioritizes opportunities, customizes applications, executes follow-ups, and continuously improves user outcomes based on feedback and historical data. The study outlines the key problems identified in modern job searching, the system architecture designed to address them, and the results observed during evaluation.
Problem Identified
Through stakeholder interviews, domain research, and analysis of existing operation workflows, several key problems were identified:
Fragmented Job Search Workflow: Job seekers rely on multiple disconnected tools for searching jobs, creating resumes, tracking applications, scheduling interviews, and following up. This fragmentation increases cognitive load and causes missed opportunities.
Poor Job Prioritization: Most platforms rank jobs using basic keyword matching, offering little insight into role fit, growth potential, or likelihood of success. Users lack clarity on which jobs to target first..
Manual and Repetitive Application Tasks: Customizing resumes, writing cover letters, filling application forms, and sending follow-ups require significant manual effort, making it difficult to scale applications without sacrificing quality.
Lack of Feedback and Visibility: Job seekers receive minimal actionable feedback from rejections and have limited visibility into application performance, skill gaps, and hiring timelines.
Developed Solution
The platform was designed as a modular, AI-driven system that automates job discovery, application execution, and continuous optimization.
1. User Interface Layerr: Provides all user-facing experiences, including::Resume and cover letter builder
Application tracker and interview calendar
Activity and analytics dashboard
Chrome extension for one-click application.
2. Intelligence & AI Layer: Powers decision-making and personalization:
Job matching and priority scoring
Resume and cover letter auto-tailoring
Interview preparation and salary negotiation assistance
Skill gap detection and learning recommendations
3. Workflow Automation Layer: A drag-and-drop automation engine enables users to:
Create job application workflows
Trigger actions such as applying, following up, and recruiter outreach
Scale applications while maintaining quality and consistency
4. Integration & Data Layer: Connects external systems and manages data:
Job boards, LinkedIn, email, calendars, and salary sources
Stores application history, recruiter interactions, and outcomes
Feeds analytics and predictive insights
Results and Evaluation
Efficiency Gains
Resume and cover letter preparation time reduced by ~80%
One-click apply significantly lowered manual effort
Improved Job Targeting
Priority scoring helped users focus on high-fit roles
Reduced applications to low-conversion jobs
Higher Response Rates
Auto-tailored applications improved ATS pass-through
Smart follow-ups increased recruiter responses
Better Career Insights
Users gained visibility into which roles, industries, and skills performed best
Skill gap analysis enabled targeted upskilling
