Unmask

Have you heard of Soham? In a world where hiring fraud is harder to detect, how can you identify bad actors quickly and affordably? We’re team Le Commit, and we built Unmask for the Vultr track after seeing how easy it’s become to fake resumes, reuse identities, and vibecode technical interviews. Fast-moving teams don’t have time for slow, outdated background checks, and the cost of a bad hire is too high. Unmask is an AI-powered credibility checker that helps hiring managers verify candidate authenticity by detecting inconsistencies across CVs, LinkedIn, GitHub, reference calls, and live interviews. It flags timeline mismatches, missing signals, and potential identity fraud. Reference calls are automated using voice APIs, with transcripts scored and cross-checked against prior claims. During interviews, Unmask provides real-time prompts to validate risky areas. The dashboard surfaces credibility scores, red/yellow flags, and suggested follow-ups, giving teams signal before they waste time. Built with LLaMA models, Groq API, React, Tailwind, and deployed on Vultr using a modular agent pipeline. Like at http://unmask.click/ Repo https://github.com/mousberg/le-commit