[ 03 ] - RECRUITMENT
Overview
How a high-volume recruitment firm replaced manual CV screening with a custom semantic scoring engine. This system doesn't just look for keywords; it understands candidate experience and intent, ranking applicants against specific role requirements in real-time.
Category
DATA INTELLIGENCE
Privacy Disclaimer
To ensure security and confidentiality, case studies utilize anonymized data unless authorized.
Challenge
A boutique recruitment agency was struggling with "Information Overload." For every open mandate, they were receiving over 500 applications, 80% of which were unqualified. The staff was spending roughly 40 hours per week performing initial screenings—a manual task that led to "decision fatigue" and delayed the submission of top-tier talent to clients. By the time a human recruiter identified a "perfect match," that candidate had often already been placed by a faster competitor. The firm was physically unable to scale its placement volume without hiring more expensive administrative staff.
Impact
The deployment of the Talent Intelligence Engine shifted the firm from a "Search" model to a "Selection" model. - 90% Screening Efficiency: The system processes 500+ resumes in seconds, delivering a "Top 10" shortlist to the recruiter’s desk before they even finish their morning briefing. - Semantic Matching: Unlike basic keyword filters, the engine understands "Skill Density," allowing it to find high-value candidates that traditional software would have missed. - Instant Engagement: Qualified candidates receive an automated, personalized outreach within 5 minutes of applying, securing their interest before the market reacts. - Increased Placement Velocity: The firm reported a 40% increase in monthly placements because recruiters spent 100% of their time interviewing rather than reading resumes.
![](data:image/svg+xml,<svg display="block" role="presentation" viewBox="0 0 824 736" xmlns="http://www.w3.org/2000/svg"><path d="M 375.73 2.589 C 378.705 0.974 382.295 0.974 385.27 2.589 L 743.927 197.234 C 751.075 201.114 750.849 211.449 743.539 215.012 L 384.882 389.864 C 382.116 391.212 378.884 391.212 376.118 389.864 L 17.461 215.012 C 10.151 211.449 9.925 201.114 17.073 197.234 Z" fill="transparent" height="392px" id="u8A38kZkZ" stroke-dasharray="0" stroke-linecap="butt" stroke-linejoin="miter" stroke-miterlimit="4" stroke-width="3" stroke="rgba(171, 171, 171, 0.2)" transform="translate(26 13)" width="761px"/><path d="M 17.948 56.548 C 10.528 53.014 10.302 42.536 17.562 38.685 L 90.04 0.244 C 90.328 0.091 90.673 0.088 90.964 0.237 L 379 147.5 L 707.51 0.219 C 707.816 0.082 708.17 0.108 708.454 0.286 L 768.198 38.02 C 774.951 42.285 774.225 52.357 766.929 55.608 L 383.21 226.624 C 380.538 227.814 377.48 227.776 374.839 226.518 Z" fill="transparent" height="228.5px" id="LLwxKZ78X" stroke-dasharray="0" stroke-linecap="butt" stroke-linejoin="miter" stroke-miterlimit="4" stroke-width="3" stroke="rgba(171, 171, 171, 0.2)" transform="translate(26 330.5)" width="784px"/><path d="M 17.948 56.548 C 10.528 53.014 10.302 42.536 17.562 38.685 L 90.04 0.244 C 90.328 0.091 90.673 0.088 90.964 0.237 L 379 147.5 L 707.51 0.219 C 707.816 0.082 708.17 0.108 708.454 0.286 L 768.198 38.02 C 774.951 42.285 774.225 52.357 766.929 55.608 L 383.21 226.624 C 380.538 227.814 377.48 227.776 374.839 226.518 Z" fill="transparent" height="228.5px" id="Vtze6meDP" stroke-dasharray="0" stroke-linecap="butt" stroke-linejoin="miter" stroke-miterlimit="4" stroke-width="3" stroke="rgba(171, 171, 171, 0.2)" transform="translate(26 483.5)" width="784px"/></svg>)