Moment 01
A job search automation platform for scraping, AI matching, cover-letter generation, and multi-channel application workflows.
Overview
Why this system had to exist
JOBMOO was built to reduce the repetitive overhead of searching roles, scoring fit, preparing outreach, and applying across multiple channels. Job seekers often spend more time collecting and filtering opportunities than actually pursuing the best ones. The platform needed to centralize that workflow and keep it visible through a dashboard.
Moment 02
Built a fullstack automation pipeline that compresses the manual job-hunting loop into a managed system.
Moment 03
Compressed the job-hunting lifecycle by automating multi-feed scraping, AI fit scoring, and application delivery.
Stage 1 of 5
Continue
Problem
What was breaking down
Job sources expose different formats, rate limits, and interaction models, which makes one unified workflow difficult. On top of that, tailoring applications manually does not scale when a candidate wants to move quickly without sacrificing quality. The system needed to scrape, score, generate materials, and submit actions while keeping progress observable and repeatable.
Moment 01
Opportunity Discovery
Manual site hopping
Moment 02
Application Prep
Manual tailoring each time
Moment 03
Execution Visibility
Scattered notes
Stage 2 of 5
Constraint map
Solution
The breakthrough and implementation path
I implemented a React and Express stack backed by MongoDB, Redis queues, and Playwright automation. The app pulls in opportunities from multiple sources, scores them against a profile, drafts supporting content, and can trigger application actions through supported channels. That turns a fragmented search process into a trackable system with real operational leverage.
Moment 01
The most valuable shift was treating job hunting like an automation pipeline rather than a list of one-off tasks. Once jobs become queued records with match scores, generated collateral, and delivery states, the operator can focus on review and strategy instead of repetitive browsing and copying.
Moment 02
React Dashboard
Moment 03
Express API + Socket Updates
Stage 3 of 5
Build system
Results
Before vs after, without the clutter
Outcome metrics stay visible while comparison details are compressed into large readable cards instead of long stacked panels.
Before
Opportunity Discovery
Manual site hopping
After
Optimized resolution
Centralized automated intake
Stage 4 of 5
Measured impact
Architecture
Layered architecture with one consistent layout
Explore the operating layers and the shipped feature set in the same wider content frame used across the full case study.
Active layer
React Dashboard
Multi-source job scraping across feeds, APIs, and browser automation
Ship 01
Multi-source job scraping across feeds, APIs, and browser automation
Ship 02
AI scoring against candidate profiles with personalized cover letters
Ship 03
Automated application delivery through email and form workflows
Ship 04
Live progress tracking via queues, sockets, and dashboard feedback
Stage 5 of 5
Architecture live