video1.0<iframe src="https://www.loom.com/embed/039aa0f549ea4b01a203a61cf1bb1fb9" frameborder="0" width="1114" height="835" webkitallowfullscreen mozallowfullscreen allowfullscreen></iframe>8351114Loomhttps://www.loom.com8351114https://cdn.loom.com/sessions/thumbnails/039aa0f549ea4b01a203a61cf1bb1fb9-980a971fc0efd4a7.gif300.1711Purpose & Impact: ReSearcher solves a $30 billion inefficiency: 40% of conference-accepted researchers can't afford $4,700 attendance while companies spend $30,000+ per hire searching for them. We built a reasoning graph autonomously matching peer-reviewed papers to job descriptions—85% cost savings while funding conference access. Deployed across major AI conferences, this could fund 5,000+ researchers annually while saving companies $150M in recruitment fees. Technical Implementation: Our dual-portal architecture uses Perplexity's sonar-pro model with return_citations: true and search_domain_filter: ['arxiv.org', 'scholar.google.com', 'github.com'] for academic grounding. Multi-step reasoning pipeline: fetchResearcherProfile() — Real-time Scholar search with authorship validation matchPaperToJob() — Semantic analysis returning 0-100 scores with alignment/gaps askAboutResearcher() — Skill extraction with citations Evidence mapping — Paper sections → job requirements Built with Next.js 14 and TypeScript, achieving ~5 second matching for 5 researchers via parallel processing (5-10x speedup) with 30-minute caching.