๐ฏ Tired of Sending Resumes Into the Void?
You’ve got the skills.
You’ve got the degree.
So why aren’t you getting interviews?
Spoiler: Your resume might be technically good…
…but it’s not speaking the language of the job you want. ๐คซ
✨ Introducing – The AI Resume Optimizer
(Built for students & new grads who want real results)
✅ Paste any job description
✅ Upload your current resume
✅ Get an instantly tailored version that:
- Highlights exactly what employers want
- Beats Applicant Tracking Systems (ATS)
- Makes your experience shine—even with little work history!
No fluff. No guesswork. Just more interviews.
๐ Perfect for:
- Final-year students
- Recent grads
- Internship & entry-level applicants
- Career switchers
๐ฅ Special Launch Offer for Students!
Get 3 free optimizations this month →
No credit card needed.
๐ Try it now – it takes 60 seconds!
๐ Optimize My Resume Free
๐ฌ “I used it for a Google internship app… got a call in 3 days!”
— Priya R., 3rd-year CS student
“Finally, a tool that doesn’t assume I have 5 years of experience!”
— Malik T., Recent Business Grad
๐ Share with a friend who’s job hunting!
#StudentJobs #ResumeHelp #CareerTips #GraduateJobs #JobSearch #ATS #ResumeOptimizer #CampusToCareer #JobBank #UniversityLife
๐ Visual Recommendations (for your designer or Canva):
- Primary Image/Video:
- Split-screen: Left = sad student staring at “Application Received” email; Right = same student smiling with “Interview Scheduled!”
- Or: Animated demo (15 sec) showing resume transforming with AI
- Colors: Bright, hopeful (teal + orange or university-brand colors)
- Text Overlay: “Your resume vs. The Job Description → Let AI bridge the gap!”
## **1. Executive Summary**
Many job seekers face challenges in customizing their resumes for individual job postings, leading to lower interview conversion rates. The **AI Resume Optimizer** leverages natural language processing (NLP) and machine learning to analyze job descriptions and automatically tailor resumes to match employer expectations—highlighting relevant skills, experiences, and keywords while maintaining professional formatting.
## **2. Problem Statement**
- Generic resumes fail to pass Applicant Tracking Systems (ATS).
- Manual customization is time-consuming and skill-intensive.
- Job seekers often lack awareness of industry-specific keywords or optimal phrasing.
## **3. Proposed Solution**
An AI-powered web and mobile application that:
- Parses uploaded resumes and job descriptions.
- Recommends or auto-generates tailored resume versions.
- Scores resumes based on ATS compatibility.
- Provides actionable feedback (e.g., “Add ‘project management’,” “Quantify achievements”).
## **4. Target Users**
- Individual job seekers (students, professionals, career switchers)
- Career counselors and university placement cells
- Small recruitment agencies (as a value-add tool)
## **5. Key Benefits**
- ↑ Resume-to-interview conversion rate
- ↓ Time spent per application
- ↑ Confidence in application quality
- Accessibility for non-native English speakers
## **6. Technology Stack (Preliminary)**
- **Frontend**: React.js / React Native (web + mobile)
- **Backend**: Node.js with Express
- **AI/ML**: Python (spaCy, transformers, scikit-learn), Hugging Face models
- **Database**: PostgreSQL + Cloud Storage (for resume files)
- **Deployment**: Docker + AWS/GCP
---
# **Software Requirements Specification (SRS)**
*Based on IEEE 830 Standard*
## **1. Introduction**
### 1.1 Purpose
This document specifies functional and non-functional requirements for the **AI Resume Optimizer**, enabling developers to design, implement, and test the system.
### 1.2 Scope
The system allows users to:
- Upload a resume (PDF/DOCX).
- Input or paste a job description.
- Receive a tailored resume with optimization suggestions.
- Download or share the optimized version.
It does **not** include job search functionality or direct application submission.
### 1.3 Definitions
- **ATS**: Applicant Tracking System
- **NLP**: Natural Language Processing
- **CV/Resume**: Document summarizing work experience, education, and skills
---
## **2. Overall Description**
### 2.1 User Classes
| User Type | Needs |
|---------|------|
| Job Seeker | Customize resume quickly, improve ATS score |
| Career Advisor | Review and export optimized resumes for clients |
| Admin | Manage system performance, monitor usage |
### 2.2 Operating Environment
- Web browser (Chrome, Firefox, Safari, Edge)
- Mobile apps (iOS & Android)
- Internet connection required for AI processing
### 2.3 Assumptions & Dependencies
- Users have a baseline resume to upload.
- Job descriptions are in English (initial release).
- Third-party libraries for PDF/DOCX parsing (e.g., PyPDF2, python-docx).
---
## **3. Functional Requirements**
| ID | Requirement | Description |
|----|------------|-------------|
| FR1 | User Registration/Login | Users can sign up via email or Google OAuth. |
| FR2 | Resume Upload | Support PDF and DOCX uploads (<5 MB). |
| FR3 | Job Description Input | Paste text or upload a job posting (PDF/URL parsing planned for v2). |
| FR4 | Resume Analysis | Extract skills, experience, education, and keywords from resume. |
| FR5 | Job Description Analysis | Identify required skills, qualifications, and keywords. |
| FR6 | Gap Detection | Compare resume vs. job description; flag missing keywords or weak sections. |
| FR7 | Resume Optimization | Suggest edits or auto-generate an optimized version. |
| FR8 | ATS Compatibility Score | Provide a score (0–100) and explanation. |
| FR9 | Export & Download | Allow download in original format (PDF/DOCX) with edits. |
| FR10 | Revision History | Store last 5 versions per user. |
---
## **4. Non-Functional Requirements**
| Category | Requirement |
|--------|-------------|
| **Performance** | Resume analysis completed within 8 seconds (95% of requests). |
| **Security** | All user data encrypted at rest and in transit (TLS 1.3+). Resumes not shared with third parties. |
| **Usability** | WCAG 2.1 AA compliant. Onboarding tutorial for first-time users. |
| **Reliability** | 99.5% uptime (excluding scheduled maintenance). |
| **Scalability** | Support 10,000 concurrent users by v1.2. |
| **Maintainability** | Modular codebase; CI/CD pipeline with automated testing. |
| **Privacy** | GDPR/CCPA compliant. Users can delete data permanently. |
---
## **5. System Features**
### 5.1 Core Features
- **Smart Keyword Matching**: Uses semantic similarity (not just exact matches).
- **Tone & Verb Optimization**: Recommends action verbs (e.g., “Led” → “Spearheaded”).
- **Achievement Quantification**: Prompts user to add metrics (e.g., “Increased sales by 30%”).
- **Template Suggestion**: Recommends clean, ATS-friendly layouts.
### 5.2 Future Enhancements (v2+)
- LinkedIn profile import
- Multi-language support
- Cover letter generator
- Integration with job boards (Indeed, LinkedIn)
---
## **6. Development Readiness Checklist**
✅ Clear problem-solution fit
✅ Defined user personas
✅ Functional & non-functional specs finalized
✅ Tech stack selected
✅ Data privacy strategy in place
✅ MVP scope defined (web app + core AI engine)
**Next Steps**:
1. Design UI/UX wireframes
2. Build NLP pipeline prototype
3. Develop backend API
4. Conduct alpha testing with 50 job seekers
---
Let me know if you'd like this in **PDF format**, **pitch deck slides**, or need a **Facebook marketing post** for this product (similar to your previous request for the email tool)!
Absolutely, Yew! To **transition smoothly from planning to development**, your team needs **clear, actionable, and prioritized requirements** that developers, designers, and QA engineers can directly implement. Below is a **development-ready breakdown** of the AI Resume Optimizer—structured for immediate sprint planning.
---
## ✅ **Development-Ready Requirements Summary**
### ๐ฏ **Goal of MVP (Minimum Viable Product)**
Enable job seekers to upload a resume + job description → receive an optimized, downloadable resume with an ATS compatibility score.
**MVP Scope (v1.0 – Web Only)**
- No mobile app (React Native deferred to v2)
- English only
- No user accounts (anonymous sessions with optional email save)
- Resume output as downloadable DOCX (PDF support in v1.1)
---
## ๐ **Prioritized Feature Backlog (Ready for Sprint 1)**
| Priority | Feature | User Story | Acceptance Criteria |
|--------|--------|-----------|---------------------|
| **P0** | **Resume Upload** | As a user, I can upload my resume (PDF/DOCX) so the system can analyze it. | - Supports .pdf and .docx<br>- Max file size: 5 MB<br>- Parses text accurately (tested on 20 sample resumes)<br>- Shows preview of extracted text |
| **P0** | **Job Description Input** | As a user, I can paste a job description so the AI can tailor my resume. | - Text area accepts ≥2000 chars<br>- Auto-detects and trims fluff (e.g., “About Us” sections)<br>- Highlights detected role title and key requirements |
| **P0** | **Core AI Analysis Engine** | As a user, I want the system to compare my resume to the job description and find gaps. | - Extracts: skills, job titles, education, years of experience<br>- Matches using semantic similarity (e.g., “Python” ≈ “Python scripting”)<br>- Flags missing hard skills (e.g., “AWS not found in resume”) |
| **P0** | **Resume Optimization Output** | As a user, I receive a tailored resume with clear improvements. | - Generates a new DOCX file with:<br> • Reordered bullet points (most relevant first)<br> • Suggested edits in [brackets] or comments<br> • Added missing keywords (only if contextually valid)<br>- Preserves original formatting as much as possible |
| **P0** | **ATS Score & Report** | As a user, I see how ATS-friendly my resume is. | - Displays score (0–100)<br>- Shows top 3 improvement tips (e.g., “Add ‘Agile methodology’”, “Include job title in summary”) |
| **P1** | **Anonymous Session + Email Save** | As a user, I can get a link to access my result later (optional). | - On result page: “Email me this result”<br>- Sends secure link (e.g., `yourapp.com/r/abc123`) with 7-day expiry |
| **P2** | **Basic Dashboard (Post-MVP)** | — | Deferred to v1.1 |
---
## ๐ง **Technical Specifications for Dev Team**
### **Frontend (React.js)**
- **Pages**:
1. Landing + Upload (`/`)
2. Job Description Input (`/job`)
3. Processing Screen (`/processing`)
4. Results + Download (`/result`)
- **Libraries**:
- `react-dropzone` for file upload
- `docxtemplater` or `mammoth` + `docx` for DOCX generation
- `axios` for API calls
- **UI Framework**: Tailwind CSS (for rapid dev)
### **Backend (Node.js + Python Microservice)**
- **Node.js (API Layer)**
- Routes: `POST /upload-resume`, `POST /analyze`, `GET /result/:id`
- Stores session data in Redis (TTL: 24h)
- **Python (AI Service)**
- Input: parsed resume JSON + job description text
- Processing:
- Resume parsing: `PyPDF2` + `python-docx`
- NLP: `spaCy` (en_core_web_lg) + `sentence-transformers/all-MiniLM-L6-v2` for semantic matching
- Keyword extraction: custom rules + SKILLS taxonomy (preloaded list: e.g., ["Python", "AWS", "Project Management"])
- Output:
```json
{
"ats_score": 82,
"missing_keywords": ["Docker", "CI/CD"],
"optimized_content": { ... }, // structured sections
"suggestions": ["Quantify your impact in Project X", "Move 'Team Lead' experience higher"]
}
```
### **Data Model (Simplified)**
```json
Session {
id: string (UUID),
resume_text: string,
job_description: string,
analysis_result: JSON,
created_at: timestamp,
expires_at: timestamp
}
```
---
## ๐ **Key Non-Functional Requirements (Enforced from Day 1)**
1. **Privacy by Design**
- Resumes are **never stored permanently** unless user opts in via email save.
- All processing data auto-deleted after 24 hours.
2. **Error Handling**
- Clear messages for: unsupported file, parsing failure, AI timeout.
3. **Rate Limiting**
- 3 analyses/hour per IP (to manage AI compute costs during MVP).
4. **Logging**
- Log anonymized usage metrics (e.g., “Job title: ‘Software Engineer’ → avg. ATS score: 68”) for model improvement.
---
## ๐ **First 2-Week Sprint Plan**
| Day | Task |
|-----|------|
| 1–2 | Set up repo, CI/CD, project board (GitHub Projects) |
| 3–5 | Build frontend upload + job input screens |
| 6–8 | Develop resume parser (Python) + test on 50 real resumes |
| 9–11 | Build semantic matcher + ATS scoring logic |
| 12–13 | Integrate frontend ↔ backend ↔ AI service |
| 14 | Internal QA: test 10 end-to-end user flows |
**Success Metric for MVP**:
> 80% of beta users (n=30) say the optimized resume “feels more relevant” than their original.
---
## ๐ Deliverables Ready for Handoff to Dev Team
- ✅ Prioritized user stories with acceptance criteria
- ✅ API contract (request/response samples)
- ✅ Data model
- ✅ Tech stack & library choices
- ✅ Privacy & error handling rules
---
If you approve this scope, your team can **start coding tomorrow**. Would you like:
- A **GitHub issue template** for each P0 feature?
- A **Figma wireframe** for the 4 core screens?
- **Sample resume/job description pairs** for testing?
Let me know how you’d like to proceed!
