AI-Powered smart resume builder that customizes resumes to align with specific job descriptions (JDs). Users can upload their professional details (projects, work experience, profiles, etc.), and with each job description, the system leverages AI to parse the requirements and adapt the user's resume content accordingly.

Automated resume screening workflow built using n8n. This project integrates resume parsing, candidate evaluation, and AI-powered scoring to streamline the recruitment pipeline.

An automated talent acquisition pipeline designed to streamline candidate screening using Agentic AI. This project uses LLMs to parse resumes and job descriptions, ranking candidates based on semantic alignment, with full observability provided by LangSmith.

AI-Based Resume Parser (GUI Based) This project is an AI-powered resume parser and scoring system built with Python. It allows recruiters or institutions to automatically score and filter resumes based on relevance to specific job categories using keyword matching and a graphical interface (GUI).

This repository contains a resume parsing and matching project. It extracts text from resumes, parses candidate information, matches resumes to job descriptions, and generates explainable AI (XAI) reports. The project appears to include multiple entry points and utilities for parsing, indexing, matching and producing reports.

"AI Resume Parser using NLTK: A Jupyter notebook project that utilizes Natural Language Toolkit (NLTK) and keyword analysis to identify the most suitable candidate for a job position based on their resume."'

A full-stack Next.js application designed to provide users with deep, AI-driven insights into their resumes. This platform allows users to upload their resumes, securely stores them, and uses semantic AI parsing to evaluate their experience and projects against industry standards.

HireSense is an AI-powered resume intelligence platform that goes beyond traditional ATS filtering by using semantic transformer models to deeply understand candidate profiles. It parses resumes to extract structured skills, technologies, and project experience based on real contextual meaning rather than keyword matching.

The Resume Analyzer project is a Python-based application designed to automate the process of parsing resumes, extracting relevant details, and ranking them based on job descriptions. It leverages cutting-edge technologies such as OpenAI's GPT models, LangGraph and Azure's AI Document Intelligence to process and understand resumes efficiently.

The Resume Parser AI Project automates resume screening by evaluating resumes for contextual relevance. It processes and cleans data, extracts features, and uses machine learning for classification and scoring. The goal is to enhance efficiency and accuracy in resume evaluation.

Full-stack Next.js project integrating resume parsing and Google Scholar scraping to suggest tailored projects using AI and automation.

"AI Resume Parser using NLTK: A Jupyter notebook project that utilizes Natural Language Toolkit (NLTK) and keyword analysis to identify the most suitable candidate for a job position based on their resume.

GitHub Repository Description: "AI Resume Parser using NLTK: A Jupyter notebook project that utilizes Natural Language Toolkit (NLTK) and keyword analysis to identify the most suitable candidate for a job position based on their resume."

AI-powered CV parser and ranking system built with Azure OpenAI. Automatically extracts candidate information from PDF resumes and scores them based on job requirements. Final hackathon project for Azure AI Summer School.

A Python-based resume parser application built with LangChain and Streamlit that extracts structured information from PDF, DOCX, and TXT resume files. The tool uses Google's Gemini AI model to parse resumes and extract key details like personal information, skills, education, experience, and projects into a clean JSON format.

⚙️ An AI-powered Resume Parser that extracts key sections like Skills, Experience, Education, and Projects from uploaded PDF resumes using Flask and PyMuPDF. Helps streamline candidate profiling with clean, structured output. Built with Python and a modern web UI.

I Developed AI_Resume_Analyzer as my final year project which helps the candidates parse their resumes and extract the skills and key points which may be useful for them to apply for various job roles. Parsing is done using NLP

TeamBanalo is a comprehensive platform designed to supercharge team formation in hackathons through AI-driven teammate recommendations, intelligent resume parsing, and seamless project management.

A lightweight and efficient AI-powered resume screening app built with Streamlit, docx2txt, and PyPDF2. This project helps HR teams and recruiters automatically parse and evaluate resumes against predefined job roles based on keyword matching.

This project is designed to help users create resumes and polish them with AI-powered assistance. Users can upload their existing resumes to the web page, which automatically parses and populates the resume information. The system then generates a resume in a user-defined style and supports one-click full-text translation.

This project focuses on building an AI-powered resume parsing and analysis system using Natural Language Processing (NLP) and machine learning techniques. The system is designed to automatically extract key information from resumes, such as personal details, education, work experience, skills, certifications, and projects.

CVMaster AI is an intelligent application designed to analyze resumes and provide valuable feedback. It parses the resume to generate a resume score, offers skill suggestions, and recommends relevant YouTube videos to help improve the candidate's skills. The project is built using Python, Streamlit, Pandas, Pyreparser, and NLTK.

This project is an AI-powered resume screening system that helps HR teams and recruiters efficiently manage candidate applications. The system can parse resumes, extract essential information, and evaluate candidates based on predefined criteria using machine learning and natural language processing

A Streamlit-based web app that uses OpenAI's GPT-4o-mini to analyze resumes and provide personalized, AI-generated feedback. Built as a practice project to explore NLP, PDF parsing, and API integration.

Made for the recruiting company HUNAR.ai, this project is an inbuilt machine to ease the load of hiring using a trained machine for parsing the information, skills and experience of the candidates from resume

AI-powered resume analyzer with instant ATS scoring and optimization feedback. Features dual-engine support (Groq Cloud + Ollama Local), multi-format parsing (PDF/DOCX/Images), domain-specific reviews, and intelligent project suggestions. Modern React UI with privacy-first offline mode.

Talent Flow ATS – App Blueprint 🚀👥 An AI-powered applicant tracking system to manage job requisitions 📄, parse resumes 🤖, track candidates through smart pipelines 🔄, search talent fast 🔍, and rate projects & courses ⭐. Built with NextJS, TypeScript & Tailwind ⚡

AI Based Resume Parser Project

An AI-powered web application that analyzes resumes and ranks candidates based on their relevance to a given job description. Built using Streamlit, NLP, and semantic similarity models, this tool helps recruiters quickly identify the most suitable candidates.

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