An AI-powered job-search intelligence and automation platform that continuously discovers, analyzes, scores, tracks, and acts on job opportunities based on a user's profile, skills, preferences, target companies, and career goals.
This platform is designed to automate the majority of the modern job-search process.
Instead of requiring users to manually:
- Search job boards
- Check company career pages
- Find newly posted jobs
- Identify companies that are mass hiring
- Read hiring news
- Compare jobs with their profile
- Find recruiters and employees
- Write outreach messages
- Track applications
- Remember follow-ups
- Monitor target companies
the system continuously performs these tasks in the background.
Approximately 90% of the platform's operations should run as automated background jobs.
The user mainly interacts with the system to:
- Create/update their professional profile
- Define job preferences
- Select target companies
- Review recommended opportunities
- Approve important actions
- Track their job-search progress
The platform should:
- Find relevant jobs automatically
- Reduce duplicate jobs
- Understand the user's professional profile
- Personalize job recommendations
- Detect companies that are actively hiring
- Detect mass-hiring signals
- Personalize hiring news and trends
- Monitor target companies
- Identify relevant people for networking
- Generate personalized outreach
- Track applications
- Manage follow-ups
- Score opportunities
- Notify users about high-value opportunities
- Automate repetitive job-search tasks
┌─────────────────────┐
│ USER │
│ │
│ Profile │
│ Skills │
│ Preferences │
│ Target Companies │
└──────────┬──────────┘
│
▼
┌─────────────────────┐
│ PWA / WEB │
│ │
│ Dashboard │
│ Jobs │
│ Companies │
│ News & Trends │
│ Applications │
│ Outreach │
└──────────┬──────────┘
│
▼
┌─────────────────────┐
│ API LAYER │
│ │
│ Authentication │
│ Profile │
│ Jobs │
│ Companies │
│ Applications │
│ Campaigns │
└──────────┬──────────┘
│
▼
┌────────────────────────────────────┐
│ AUTOMATION ENGINE │
│ │
│ Scheduled Jobs │
│ Queue Workers │
│ AI Analysis │
│ Scoring │
│ Deduplication │
│ Notifications │
└────────────────┬───────────────────┘
│
┌────────────────────┼────────────────────┐
▼ ▼ ▼
┌─────────────┐ ┌─────────────┐ ┌─────────────┐
│ Job Sources │ │ News Sources │ │ Company │
│ │ │ │ │ Sources │
│ ATS │ │ News │ │ Career Pages│
│ Career Pages│ │ Blogs │ │ Hiring Data │
│ APIs │ │ RSS │ │ │
└─────────────┘ └─────────────┘ └─────────────┘
🏗️ Suggested Technology Stack
Frontend
React / Next.js
TypeScript
Tailwind CSS
PWA
TanStack Query
Zustand or equivalent state management
Backend
NestJS
Node.js
TypeScript
REST API
WebSocket/SSE where required
Database
PostgreSQL
Primary data includes:
Users
Profiles
Skills
Jobs
Companies
Job sources
Applications
Contacts
Outreach campaigns
News
Trends
Notifications
Scores
Automation history
Cache / Queue
Redis
BullMQ
Used for:
Background jobs
Scheduled jobs
Rate limiting
Caching
Retry mechanisms
Distributed workers
AI
AI can be used for:
Job classification
Skill extraction
Job summarization
Job matching
News classification
Trend detection
Company analysis
Outreach personalization
Resume/job compatibility analysis
🔄 Core Automation Philosophy
The platform should be event-driven and worker-based.
The API should NOT perform expensive operations synchronously.
Instead:
User/API Event │ ▼ Create Job │ ▼ Queue │ ▼ Worker │ ▼ Process │ ▼ Database │ ▼ Notification / Next Job
Example:
New Job Found ↓ Normalize ↓ Deduplicate ↓ Extract Skills ↓ Classify ↓ Calculate Match Score ↓ Check User Preferences ↓ Check Target Company ↓ Generate Recommendation ↓ Notify User
🔎 Job Discovery Engine
The system continuously discovers jobs from multiple sources.
Possible Sources
ATS Platforms
Greenhouse
Lever
Ashby
SmartRecruiters
Other public ATS systems
Company Career Pages
The system can maintain a list of company career pages and periodically check them.
Other Sources
Public job APIs
RSS feeds
Public feeds
Search/index sources
Other legally accessible public sources
🌐 Job Data Collection
Each source should have a dedicated collector.
Greenhouse Collector Lever Collector Ashby Collector SmartRecruiters Collector Career Page Collector API Collector RSS Collector │ ▼ Raw Job Data │ ▼ Normalization Pipeline
🧹 Job Normalization
Different sources may describe the same job differently.
Example:
Senior Software Engineer Senior Software Engineer - Backend Backend Engineer Software Engineer - Node.js
The system should normalize:
Job title
Company
Location
Remote status
Employment type
Salary
Skills
Description
Requirements
Experience
Source
Application URL
Posted date
Updated date
♻️ Deduplication
Duplicate jobs should NOT appear repeatedly.
The system should identify duplicates using multiple signals.
Possible duplicate keys:
company_id + normalized_job_title + normalized_location + source + external_job_id
Additional similarity checks can include:
Job description similarity
Application URL
Company
Location
Salary
Job title
🔗 Canonical Job Model
A job should have one canonical internal record even if it exists on multiple sources.
Example:
Canonical Job │ ├── Greenhouse Source ├── Linked Source └── Company Career Page
The system should preserve source information rather than losing it.
🧠 Job Intelligence Engine
Every discovered job should pass through an analysis pipeline.
Job ↓ Extract Skills ↓ Extract Experience ↓ Extract Location ↓ Extract Salary ↓ Identify Role ↓ Identify Seniority ↓ Identify Employment Type ↓ Compare with User ↓ Generate Score
🎯 Personalized Job Matching
Each user has a professional profile.
Example:
{ "role": "Full Stack Developer", "experience": 2, "skills": [ "React", "Node.js", "NestJS", "TypeScript", "MongoDB", "PostgreSQL" ], "locations": [ "Hyderabad", "Pune", "Remote" ], "minimum_salary": 600000 }
The matching engine compares every job against this profile.
📊 Job Score
Each job receives a relevance score.
Example:
Job Score =
Skill Match
- Experience Match
- Location Match
- Salary Match
- Role Match
- Seniority Match
- Company Preference
- Employment Preference
Example:
92/100 🔥 Excellent Match
📰 News & Trends Intelligence
The platform should continuously collect hiring-related news and trends.
However, the news feed must NOT be generic.
It should be personalized based on the user's professional profile.
👨💻 Example: Developer
If the user is a developer, relevant intelligence might include:
Software engineering hiring
Backend hiring
AI engineering hiring
Full-stack hiring
Developer demand
Technology hiring trends
Companies expanding engineering teams
Engineering layoffs
Engineering mass hiring
Marketing news should not dominate the developer's feed.
📣 Example: Marketing Professional
If the user is a marketing professional, the system should prioritize:
Marketing hiring
Growth roles
Digital marketing
Performance marketing
Brand hiring
Marketing team expansion
Marketing layoffs
Marketing mass hiring
🧠 Personalized News Pipeline
News Sources ↓ News Collector ↓ Classification ↓ Extract:
- Company
- Industry
- Role
- Skills
- Location
- Hiring Signal ↓ Compare With User Profile ↓ Relevance Score ↓ Personalized Feed
📈 Mass Hiring Intelligence
One of the major features.
The platform should detect when a company appears to be significantly increasing hiring.
Signals can include:
Company Job Count ↓ Historical Job Count ↓ Compare Growth ↓ Detect Hiring Spike ↓ Analyze Departments ↓ Identify Roles ↓ Generate Hiring Signal
Example:
🚨 Hiring Surge Detected
Company: XYZ
Current openings: 84 30 days ago: 31
Growth: +171%
Primary hiring areas:
- Engineering
- AI
- Product
Your relevance: 94%
🏢 Target Company Campaigns
Users can create company campaigns.
Example:
Campaign: "Target Hyderabad Product Companies"
Companies:
- Company A
- Company B
- Company C
- Company D
The platform continuously monitors them.
🔍 Company Monitoring
For each target company:
Monitor: ├── New jobs ├── Job count ├── Hiring growth ├── Departments hiring ├── Locations ├── News ├── Funding ├── Expansion ├── Layoffs └── Relevant contacts
🤝 Networking Intelligence
The platform can identify potentially useful contacts related to an opportunity.
Examples:
Recruiters
Hiring managers
Engineering managers
Team leads
Employees in the target department
Alumni
Relevant connections
The system should rank contacts based on relevance.
✉️ Outreach Automation
Users can maintain predefined outreach templates.
Examples:
Recruiter Outreach Referral Request Hiring Manager Outreach Cold Email Follow-up Thank You Interview Follow-up
Templates should support dynamic variables.
Example:
Hi {{first_name}},
I noticed that {{company}} is hiring for {{job_title}}.
My experience with {{matching_skill}} closely aligns with the role...
🧠 AI-Personalized Outreach
Instead of sending the exact same message to everyone:
Template + Person Information + Company Information + Job Information + User Profile ↓ AI ↓ Personalized Message
📬 Outreach Tracking
Track:
Draft ↓ Ready ↓ Sent ↓ Follow-up Required ↓ Replied ↓ Interested ↓ Not Interested
📋 Application Tracking System
Every application should have a lifecycle.
Saved ↓ Applied ↓ Application Viewed ↓ Recruiter Contacted ↓ Assessment ↓ Interview ↓ Technical Round ↓ HR Round ↓ Offer ↓ Rejected
Users should also be able to manually update the status.
⏰ Follow-up Automation
The system should automatically identify applications requiring follow-up.
Example:
Application: XYZ Company
Applied: 10 days ago
No response
→ Follow-up recommended
The system can generate the follow-up message automatically.
⚡ Auto Apply / Assisted Apply
The platform can support application automation where technically and legally appropriate.
Possible modes:
Manual Assisted Semi-Automated Automated
Recommended architecture:
Job ↓ Eligibility Check ↓ Application Form Detection ↓ Resume Selection ↓ Question Extraction ↓ AI Answer Suggestions ↓ User Review / Approval ↓ Submission
The system should maintain an application audit trail.
🔔 Notification Engine
Users should receive notifications only for meaningful events.
Examples:
🔥 Excellent Job Match
🚨 Company Hiring Surge
📰 Important Hiring News
🎯 Target Company Opened New Role
📩 Follow-up Required
🤝 Relevant Networking Opportunity
💼 Application Status Changed
Notification channels can include:
In-app
Push notifications
Other supported channels
🧵 Background Job System
The majority of the platform operates through background workers.
Example workers:
JobDiscoveryWorker JobNormalizationWorker JobDeduplicationWorker JobAnalysisWorker JobScoringWorker
CompanyMonitoringWorker MassHiringDetectionWorker
NewsDiscoveryWorker NewsClassificationWorker TrendDetectionWorker
ContactDiscoveryWorker OutreachGenerationWorker FollowupWorker
NotificationWorker
ApplicationTrackingWorker
⏱️ Scheduled Jobs
Example schedules:
Every 5 minutes → Check critical queues
Every 15 minutes → Check selected job sources
Every hour → Process new jobs
Every 6 hours → Analyze company hiring changes
Every 12 hours → Discover relevant news
Daily → Recalculate scores
Daily → Check follow-ups
Weekly → Generate job-search intelligence report
Schedules should be configurable.
🔄 Example Complete Automation Flow
INTERNET │ ┌──────────────┼──────────────┐ ▼ ▼ ▼ Job Sites Companies News │ │ │ └──────────────┼──────────────┘ ▼ DATA COLLECTORS │ ▼ RAW DATA │ ▼ NORMALIZATION │ ▼ DEDUPLICATION │ ▼ AI ANALYSIS │ ┌────────────┼────────────┐ ▼ ▼ ▼ Jobs Companies News │ │ │ ▼ ▼ ▼ Scoring Hiring Relevance │ Signals Score │ │ │ └────────────┼────────────┘ ▼ USER PERSONALIZATION │ ▼ RECOMMENDATION ENGINE │ ┌────────────┼────────────┐ ▼ ▼ ▼ Jobs News Actions │ │ │ ▼ ▼ ▼ Dashboard News Feed Outreach │ ▼ Notifications
🗄️ Core Database Entities
Initial entities:
User UserProfile Skill UserSkill Preference
Company CompanySource CompanyMetric CompanyHiringSignal
Job JobSource JobSnapshot JobSkill JobScore
News NewsSource NewsTopic NewsScore
Campaign CampaignCompany
Contact ContactCompany ContactJob
Outreach OutreachTemplate OutreachMessage
Application ApplicationEvent ApplicationNote
Notification
AutomationJob AutomationExecution
🧩 Important Data Relationships
User │ ├── Profile ├── Preferences ├── Skills ├── Applications ├── Campaigns ├── Contacts └── Notifications
Company │ ├── Jobs ├── News ├── Hiring Signals ├── Contacts └── Campaigns
Job │ ├── Company ├── Skills ├── Sources ├── Scores └── Applications
🛡️ Reliability
Background automation must be designed for failure.
Every worker should support:
Retry
Exponential backoff
Timeout
Idempotency
Dead-letter queue
Logging
Monitoring
Error tracking
Example:
Worker ↓ Failure ↓ Retry #1 ↓ Failure ↓ Retry #2 ↓ Failure ↓ Retry #3 ↓ Dead Letter Queue ↓ Alert / Manual Review
🔐 Security
The platform should implement:
Authentication
Authorization
Secure sessions
Encrypted sensitive data
API rate limiting
Input validation
Secure secrets management
Audit logs
Role-based access where required
Credentials for external services should NEVER be stored as plain text.
🧪 Observability
Every automation job should be observable.
Track:
Job ID Worker Started At Completed At Duration Status Retry Count Error Input Output
Recommended monitoring:
API errors
Queue failures
Worker failures
Scraper failures
AI failures
Database performance
External API failures
Notification failures
📊 User Dashboard
The dashboard should provide a high-level overview.
Example:
JOB INTELLIGENCE
🔥 Recommended Jobs 12
🎯 Excellent Matches 5
🚨 Hiring Surges 3
📰 Relevant Trends 8
📩 Follow-ups 4
🏢 Target Companies 17
🎯 Recommendation Feed
The primary feed should prioritize high-value information.
Example:
🔥 96% Match
Senior Full Stack Engineer XYZ Technologies
Hyderabad ₹8L - ₹12L
Why this matches: ✓ React ✓ Node.js ✓ TypeScript ✓ 2+ years experience ✓ Hyderabad ✓ Salary above preference
[View Job] [Save] [Apply]
📰 Intelligence Feed
Example:
🚨 Hiring Intelligence
ABC Technologies increased engineering openings by 140% over the last 30 days.
Your relevance: 91%
[View Company]
🤖 AI Agent Architecture
The system can eventually be divided into specialized agents.
ORCHESTRATOR │ ┌────────────────┼────────────────┐ ▼ ▼ ▼ Job Intelligence Company Intelligence News Intelligence Agent Agent Agent │ │ │ └────────────────┼────────────────┘ │ ▼ Outreach Agent │ ▼ Application Agent │ ▼ Notification Agent
Agents should NOT directly perform arbitrary actions.
They should operate through controlled tools and workflows.
🔄 Event-Driven Architecture
Example events:
JOB_DISCOVERED
JOB_UPDATED
JOB_DUPLICATE_DETECTED
JOB_SCORED
HIGH_MATCH_JOB_FOUND
COMPANY_HIRING_SURGE_DETECTED
NEWS_DISCOVERED
RELEVANT_NEWS_FOUND
APPLICATION_CREATED
APPLICATION_STATUS_CHANGED
FOLLOWUP_DUE
CONTACT_FOUND
OUTREACH_GENERATED
OUTREACH_SENT
Events can trigger downstream workers.
🧠 Personalization Engine
The platform should continuously understand the user's career profile.
Inputs:
Resume Skills Experience Previous Applications Saved Jobs Rejected Jobs Applied Jobs Target Companies Preferred Locations Salary Expectations Preferred Roles Career Goals
The system can use these signals to improve recommendations.
📈 Learning From User Behavior
User actions can improve future recommendations.
Example:
User repeatedly saves Backend Jobs ↓ System detects preference ↓ Backend roles receive higher ranking
Similarly:
User repeatedly ignores Marketing Jobs ↓ Marketing relevance decreases
This should supplement, not blindly override, explicit user preferences.
🏢 Company Intelligence
For every company, maintain an intelligence profile.
Company │ ├── Current Openings ├── Historical Openings ├── Hiring Growth ├── Departments ├── Locations ├── Technologies ├── Hiring Signals ├── News ├── Funding ├── Expansion └── Relevant Contacts
📅 Weekly Intelligence Report
The platform can automatically generate a weekly report.
Example:
YOUR WEEKLY JOB INTELLIGENCE
New relevant jobs: 147
Excellent matches: 21
Target companies hiring: 8
Mass hiring signals: 4
Applications sent: 12
Interviews: 3
Follow-ups due: 5
Top hiring companies:
- Company A
- Company B
- Company C
Top emerging skills:
- AI
- TypeScript
- Cloud
💡 Product Philosophy
The application should not simply be another job board.
It should function as a:
Personal Career Intelligence & Automation System
Traditional job board:
User → Searches → Finds Job → Applies
This platform:
Internet ↓ Continuous Monitoring ↓ Data Collection ↓ AI Analysis ↓ Personalization ↓ Scoring ↓ Opportunity Detection ↓ Recommended Action ↓ Automation ↓ User
🚀 MVP Scope
The first version should focus on the core intelligence loop.
Phase 1
User
Authentication
Profile
Resume
Skills
Preferences
Target roles
Locations
Salary preferences
Jobs
Job collectors
Job normalization
Deduplication
Job database
Job search
Job scoring
Personalized recommendations
Companies
Company database
Target companies
Company monitoring
Hiring count
Automation
Scheduled collectors
Background workers
Queue system
Notifications
🚀 Phase 2
Add:
News intelligence
Hiring trends
Mass-hiring detection
Personalized news feed
Company intelligence
Advanced scoring
Weekly reports
🚀 Phase 3
Add:
Contact discovery
LinkedIn/network intelligence
Outreach templates
AI personalization
Follow-up automation
Outreach tracking
🚀 Phase 4
Add:
Application tracking
Application automation
Assisted apply
Resume optimization
AI application assistance
Advanced career analytics
🧭 Long-Term Vision
The final platform should behave like an autonomous job-search assistant.
The user should be able to say:
"I'm looking for a Full Stack Developer role in Hyderabad with at least ₹7 LPA."
The system should then continuously:
Find jobs ↓ Analyze jobs ↓ Remove duplicates ↓ Score jobs ↓ Monitor companies ↓ Detect hiring surges ↓ Read relevant hiring news ↓ Find useful contacts ↓ Prepare outreach ↓ Track applications ↓ Remind about follow-ups ↓ Generate intelligence reports
while keeping the user in control of important actions.
⭐ Core Differentiator
The platform's strongest differentiator is not simply:
"Find jobs."
It is:
"Understand the job market continuously and tell me where I should focus my career-search effort."
The system should transform raw internet data into:
DATA ↓ INFORMATION ↓ INTELLIGENCE ↓ RECOMMENDATION ↓ ACTION ↓ RESULT
📌 Final Architecture Principle
Keep the system modular.
┌─────────────────────┐ │ FRONTEND │ └──────────┬──────────┘ │ ┌──────────▼──────────┐ │ API │ └──────────┬──────────┘ │ ┌─────────────▼─────────────┐ │ DOMAIN SERVICES │ └─────────────┬─────────────┘ │ ┌─────────────▼─────────────┐ │ EVENT / QUEUE │ └─────────────┬─────────────┘ │ ┌────────────────────┼────────────────────┐ ▼ ▼ ▼ Job Workers Company Workers News Workers │ │ │ └────────────────────┼────────────────────┘ ▼ AI SERVICES │ ▼ DATABASE │ ▼ NOTIFICATIONS
The architecture should allow every major component to evolve independently without rewriting the entire application.
🏁 End Goal
Build a system where the user does not have to spend hours every day searching for opportunities.
Instead:
The system continuously searches the market, understands what is happening, identifies opportunities relevant to the individual, ranks them, and prepares the next action automatically.
That is the core vision of the platform.