TalentMetrics — The Filtering Problem | made with ngram

About the video

This video was created by ngram to explain how TalentMetrics addresses the hiring filtering problem that overwhelms recruiting teams. Using HTML/CSS motion graphics, the video opens with a hook establishing the scale of the problem: a single job posting can draw over 1,500 resumes and 500 candidates a month, with manual screening tying up roughly $75,000 a year in recruiter time. It then walks through the TalentMetrics workflow across clearly titled scenes: creating a hiring playbook that sets competencies, assessments, and scoring weights once per role; automated resume fit scoring from 0 to 100 that narrows the candidate pool by about 60 percent; automated assessments covering cognitive reading, typing, personality, voice, and video interviews for top-ranked candidates; and a candidate intelligence report that surfaces a ranked, evidence-backed recommendation, illustrated with a customer support representative example ranking in the top 12 percent. A trust scene emphasizes that scoring is deterministic and explainable, with AI organizing evidence while people retain the final hiring decision. The video closes with a call to action directing viewers to book a demo at talentmetrics AI. ngram created this video by pairing the narrated script with motion graphic scenes that visually track each stage of the hiring funnel, from problem framing through playbook setup, scoring, assessments, reporting, and trust messaging, ending on a clear next step for viewers.