Apply Boost

Inspiration...

The job application process is broken. I was spending 2-3 hours crafting personalized cover letters, tailoring resumes, and preparing for interviews for each position, only to receive generic rejection emails or complete silence. After applying to dozens of companies with minimal success, I realized there had to be a better way to leverage AI to streamline this tedious process while maintaining the personal touch that recruiters value.

What it does...

ApplyBoost is an AI-powered job application assistant that transforms any job posting into a complete, personalized application suite in under 30 seconds. Users simply upload their resume and paste a job description, and the platform generates:

Personalized Cover Letter: Professional 3-paragraph letter tailored to the specific role
Recruiter Outreach Message: LinkedIn-ready message designed to grab attention
Interview Questions: Likely screening questions with preparation guidance
Resume Feedback: Detailed analysis with ATS optimization recommendations
Fraud Detection: Built-in protection against scam job postings

How we built it...

Frontend: Built with Next.js 14, TypeScript, and Tailwind CSS for a modern, responsive interface. Used Shadcn/ui components for consistent design and Lucide React for iconography.

Backend: Flask API with Python handling all AI processing. Integrated Google's Gemini 1.5 Flash model via LiteLLM for natural language generation.

AI Integration: Implemented sophisticated prompt engineering to ensure each piece of content is specifically tailored to the job requirements while maintaining professional quality.

Memory System: Integrated Supermemory for context-aware chatbot functionality, allowing users to get personalized career advice.

Key Features:

Real-time fraud detection using AI analysis of job postings
PDF generation for cover letters and resume feedback
Copy-to-clipboard functionality for easy application submission
Responsive design that works seamlessly across devices

Challenges we ran...

API Rate Limiting: Google's Gemini free tier has strict daily limits (50 requests). Implemented development mode with mock data to continue testing and added proper error handling for quota exceeded scenarios.

Data Structure Mapping: Coordinating between frontend TypeScript interfaces and backend Python models required careful attention to ensure seamless data flow between components.

Prompt Engineering: Crafting prompts that consistently generate professional, relevant content while avoiding generic placeholders took multiple iterations and testing.

File Processing: Handling various resume formats (PDF, DOC, TXT) while maintaining reliable text extraction proved challenging, leading to a hybrid approach with manual text input fallback.

UI/UX Balance: Creating an interface that feels modern and professional while remaining intuitive for users under job search stress required careful design decisions.

Accomplishments that we're proud of...

Speed: Reduced application preparation time from 2+ hours to under 30 seconds
Quality: AI-generated content is professional and specifically tailored to each position
User Experience: Clean, intuitive interface that guides users through the process seamlessly
Fraud Protection: Implemented AI-powered fraud detection to protect job seekers from scams
Comprehensive Solution: Four essential application components in one integrated platform

What we learned...

AI Prompt Engineering: Discovered the importance of specific, detailed prompts for consistent, high-quality output
API Management: Learned to handle rate limits gracefully and implement fallback strategies
User-Centric Design: Job seekers need tools that reduce stress, not add complexity
Full-Stack Integration: Gained experience coordinating complex data flows between frontend and backend systems

What's next for ApplyBoost...

Enhanced File Processing: Implement proper PDF parsing for seamless resume upload
Template Customization: Allow users to customize tone and style preferences
Application Tracking: Add functionality to track application status and follow-ups
Analytics Dashboard: Provide insights on application success rates and improvement suggestions
Mobile App: Develop native mobile application for on-the-go job applications
Integration: Connect with job boards and ATS systems for one-click applications

Built With

Submitted to

DevHacks S2
- Winner Simplifying Hiring & Recruiting

Created by

I worked on building backend on this hack.
Here's a concise description of your hackathon project, designed for a portfolio or project summary.

Project Summary: AI Job Application Assistant
I developed an AI-driven job application assistant for a hackathon, designed to streamline the job search process. The project, named "JobSearch Second Brain," is a full-stack application with a Python Flask backend and a simple JavaScript/HTML frontend.

I used LiteLLM to create a unified API layer, allowing the backend to seamlessly integrate with Google Gemini's language model for all AI tasks. I also integrated the Supermemory API to provide long-term memory for the application, enabling it to save and retrieve past conversations and application data.

The core functionalities I built include:

Structured Data Extraction: An API that analyzes job descriptions and provides key skills, requirements, and a summary in a structured JSON format.

Resume-to-JD Mapping: A tool that compares a user's resume against a job description to identify skill matches and gaps.

AI-Powered Content Generation: APIs that automatically draft personalized recruiter messages, tailored resume snippets, and cover letters based on the user's data.

Fraud Detection: An API that uses the LLM to scan for common red flags in job postings.

Persistent Memory: Implemented a system to save all generated content and application data to Supermemory, allowing the user to search and reuse information in a future session.

Dheeraj Kumar
Precious Nyaupane
Devashish Shrestha
Tascheel Chinthagumpala

Updates

Precious Nyaupane started this project — Sep 21, 2025 02:50 PM EDT

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