Drop in a stack of resumes, define the job requirements, and let AI score, rank, and shortlist every candidate — with a clear explanation for each decision.
Also for Windows and Intel Macs · or get the source
Built to cut manual resume review by 90% — without sending your candidates' data to anyone.
Drag & drop entire folders of PDF, DOCX or TXT resumes. Duplicates are auto-detected and skipped by content hash.
Weighted scoring across keywords, skills, experience, education, projects, structure and formatting — the same rubric real ATS systems use.
Score ≥ 50 → shortlisted. Below → rejected. No manual clicking through hundreds of profiles. Export shortlisted or rejected lists to CSV.
Every candidate gets a full breakdown: matched vs missing skills, strengths, gaps, and AI-written improvement suggestions.
Plug in Groq (free), OpenAI, Anthropic Claude, or run fully offline with local Llama via Ollama. Configure in Settings — no code, no env files.
Skill distribution, experience histogram, score spread, education stats and top candidates across all your job openings.
From job description to ranked shortlist in three steps.
Role, required & preferred skills, minimum experience, education, keywords — takes two minutes.
Upload hundreds at once. Each one is parsed and scored against your exact requirements.
Ranked candidates with scores, explanations, filters and one-click CSV export for your hiring pipeline.
Version 1.0.0 · free forever · no account needed
First launch: on Mac, right-click the app → Open. On Windows, click "More info → Run anyway". (Installers are unsigned — the code is fully open for review on GitHub.)
ResumeRank runs entirely on your computer — resumes, scores and the database never touch our servers (we don't have any). The only external call is to the AI provider you choose, with your key, for parsing text. Prefer zero external calls? Use the built-in rule engine or a local Llama model via Ollama.