Aura Hire: A Multi-Modal AI Framework for Autonomous, Bias-Reduced and Proctored Technical Recruitment

Aura Hire: A Multi-Modal AI Framework for Autonomous, Bias-Reduced and Proctored Technical Recruitment | Research Square window.SnipcartSettings = { analytics: { enabled: false } }; (function() { var accessVector = localStorage.getItem('access_vector') || ''; window.dataLayer = window.dataLayer || []; if (accessVector) { window.dataLayer.push({ user: { profile: { profileInfo: { snid: accessVector } } } }); } })(); (function(w,d,s,l,i){w[l]=w[l]||[];w[l].push({'gtm.start':new Date().getTime(),event:'gtm.js'});var f=d.getElementsByTagName(s)[0],j=d.createElement(s),dl=l!='dataLayer'?'&l='+l:'';j.async=true;j.src='https://www.googletagmanager.com/gtm.js?id='+i+dl;f.parentNode.insertBefore(j,f);})(window,document,'script','dataLayer','GTM-K279D39R'); Browse Preprints In Review Journals COVID-19 Preprints AJE Video Bytes Research Tools Research Promotion AJE Professional Editing AJE Rubriq About Preprint Platform In Review Editorial Policies Our Team Advisory Board Help Center Sign In Submit a Preprint Cite Share Download PDF Research Article Aura Hire: A Multi-Modal AI Framework for Autonomous, Bias-Reduced and Proctored Technical Recruitment Alam N. Shaikh, Tulshidas R. Mane, Kartikey P. Singh, Hiya S. Chowdhury This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-9533993/v1 This work is licensed under a CC BY 4.0 License Status: Posted Version 1 posted You are reading this latest preprint version Abstract Modern educational frameworks and industry demands necessitate highly scalable and equitable hiring ecosystems. However, conventional talent acquisition workflows suffer from severe manual bottlenecks and subjective human prejudices. To resolve these systemic inefficiencies, this paper introduces Aura Hire: an autonomous artificial intelligence recruitment platform. By integrating Large Language Models (LLMs), low-latency Voice Agents, and browser-based Computer Vision, the system orchestrates a comprehensive four-stage pipeline. This framework unifies automated resume shortlisting, interactive voice-based behav-ioral interviews, real-time proctoring, and live technical coding assessments into a single web application. Crucially, the system introduces edge-computed, zero-retention biometric proctoring, fundamentally mod-ernizing the preliminary candidate screening lifecycle while establishing a standardized, bias-resistant, and securely private evaluation methodology. AI Recruitment Generative AI Voice Agents Next.js Multi-modal Analytics Bias Reduction Automated Assessment Full Text Additional Declarations No competing interests reported. Supplementary Files JournalPaperLatexfile.zip Cite Share Download PDF Status: Posted Version 1 posted You are reading this latest preprint version Research Square lets you share your work early, gain feedback from the community, and start making changes to your manuscript prior to peer review in a journal. As a division of Research Square Company, we’re committed to making research communication faster, fairer, and more useful. We do this by developing innovative software and high quality services for the global research community. Our growing team is made up of researchers and industry professionals working together to solve the most critical problems facing scientific publishing. Also discoverable on Platform About Our Team In Review Editorial Policies Advisory Board Help Center Resources Author Services Accessibility API Access RSS feed Manage Cookie Preferences © Research Square 2026 | ISSN 2693-5015 (online) Privacy Policy Terms of Service Do Not Sell My Personal Information {"props":{"pageProps":{"initialData":{"identity":"rs-9533993","acceptedTermsAndConditions":true,"allowDirectSubmit":true,"archivedVersions":[],"articleType":"Research Article","associatedPublications":[],"authors":[{"id":633321448,"identity":"6acbde75-29f5-48e1-94e1-73b9aea52fdf","order_by":0,"name":"Alam N. 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