This was our final year project , which was created to ease the process of CV-Screening by automatically selecting top candidates out of the number of Candidates applied for the job . We have used Python + Flask as backend and Angular as Frontend . The operation was quiet easy , just upload PDF of CV and get the TOP Candidates .

A brutally honest, sector-specific CV screener skill for Claude

🤖 Automated AI HR Screening Pipeline — n8n + GPT-4 + Gmail + Google Sheets | 4th Sem AI Lab Project

Локальная интеллектуальная система (FastAPI + Streamlit) для автоматического парсинга, ИИ-скоринга и сравнительного анализа резюме соискателей с использованием локальной LLM через Ollama. Полностью контейнеризировано в Docker Compose.

A Claude Agent / CoWork skill that reads, screens and rates applicant CVs from your inbox

ATS-Research

Recruiter dashboard for AI-assisted CV screening. Manage candidates, resumes, job descriptions, and evaluate applications with explainable AI scoring. Built with Next.js 14, TypeScript, and Tailwind CSS.

Browser extension that scores CVs against a Job Description. Human-in-the-loop, BYO API key, MIT licensed.

AI-powered CV screening tool — score candidate-job fit, identify gaps, suggest improvements

Automated CV pre-screening system. Upload PDF/DOCX → Claude Haiku 4.5 parses → ML predicts Invite/Reject → stored in PostgreSQL → React dashboard. AI-Powered · Fairness-First · Production-Ready.

AI-powered CV screener using Llama 3.1 via Groq. Upload a CV and job description to get a match score, matched skills, missing skills, and improvement tips. Built with Streamlit.

Upload PDF resumes and score them against configurable hiring criteria with text extraction and OCR.

AI-powered candidate screening system — n8n + Groq — scores CVs contextually, auto-sends decisions, logs everything. 8hrs of screening in 35 minutes.

AI-powered CV screening app built with SAP CAP, SAP Fiori Elements, Node.js and Groq AI — deployed on SAP BTP Cloud Foundry

Local-first, privacy-preserving CV screening API — the LLM judges semantics; deterministic code verifies every fact (contacts, years, skills, scores). Evidence-grounded, prompt-injection-screened, fairness-audited (EEOC/NYC LL144), bilingual. Runs fully offline in a zero-dependency mock mode.

AI-powered CV screening backend — NestJS, PostgreSQL, Prisma. Multi-criteria scoring engine with explainable results to help recruiters evaluate candidate fit against job descriptions.