SOURCING AI SHOWCASE - Day 2

Three sourcing technology vendors demonstrate live tools built to cut down the manual work of finding, ranking, and engaging candidates, each fielding a surprise sourcing challenge on top of a scripted demo.

• A staffing-and-technology hybrid shows a platform that builds job descriptions with generative AI (including synonym expansion, so a search for "warehouse" also surfaces candidates who describe themselves as working in a "stockroom"), then pulls from a large verified candidate database. It scores and ranks applicants against a job, shows the reasoning behind each match score, and automates outreach by text, email, video, and questionnaire, tracking drop-off at each stage of a campaign.
• A matching engine that sits inside any applicant tracking system with an open API stack-ranks incoming applicants as hot, warm, or cold against a job description, parses out hard and soft skills for quick review, and lets a recruiter advance a candidate without leaving the extension. It also mines a company's existing applicant database to resurface "silver medalist" candidates who were previously passed over but fit a new opening, and pulls in outside candidates from a large pool of public profiles when internal options run dry.
• A top-of-funnel sourcing platform positions itself around three workflows: locating qualified talent, engaging those candidates, and getting them directly onto a recruiter's calendar, aiming to compress the distance between finding a candidate and booking time with them.

Across all three demonstrations, common threads emerge: filtering by company, university, or years of experience; treating existing applicant pools as an underused source of qualified talent; and building in human oversight rather than fully automating decisions. Each tool is pitched as a way to shift recruiter time away from manually screening resumes and toward conversations with candidates who are already a strong match.