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A Fortune 500, NYSE-listed insurance carrier with 215+ locations rejected six AI hiring vendors over 18 months. They approved Nodes in 17 days, then ran a controlled study across 747 scored agents. The results challenged every assumption about what makes a good hire.
1.5 million applications a year through an Avature ATS. Recruiters could manually review about 1.5% of them. Keyword filters, insurance experience, sales background, degree, had never been validated against actual production. The pattern recognition that separated good hires from bad was retiring with the managers who built it.
Once deployed, Nodes connected ATS screening criteria to production milestones at the individual agent level. We tested each of the six standard keywords against RPA (Rookie Production Award). Insurance experience, the filter most employers apply first, has zero predictive power for RPA (p=0.56) and eliminates 80% of the people who eventually win it.
AUC is the standard measure of a binary predictor: 0.5 is random, 1.0 is perfect. The six-keyword composite performs identically to a coin flip on both milestones. Nodes Fit Score is the strongest single predictor (AUC 0.618). Combined with the one traditional signal that works, sales experience, it reaches 0.644.
Across 10,362 hires from 2022-2025, we tested whether speed-to-milestone predicts annual output. Without scoring (n=1,011, 2022-2024), the correlation was noise (r=+0.045, p=0.15). With scoring active (n=679, 2025), every bucket step slower meant lower production, the fastest 30-day hires produced 1.8× the slowest 121+ day hires. Scoring creates the relationship between speed and output that did not exist before.
The behavioral signals that separate top producers from underperformers are invisible to keyword matching. Adaptability, resilience, relationship building, none of them searchable by industry-experience or job-title match. Two candidates the ATS would have skipped became top producers. One "perfect resume" did not.
Each Fit Score is the weighted output of 28+ individual assessments across four categories, calibrated against the carrier's validated top-performer persona by location and role. A warehouse worker's "equipment repair" maps to technical aptitude; a restaurant server's "17 years customer-facing" maps to relationship building. Keywords can't do that math.
Patterns derived from career history, how someone navigates transitions, handles responsibility, and operates under pressure.
Scored against the carrier's persona. Signals mapped from adjacent roles, not just job-title matches.
Values alignment from career history and role patterns. Predicts cultural integration, not just performance.
Trajectory patterns. Are they growing in responsibility? Are their roles getting more complex over time?
Eleven metrics your compliance team is already asking about. Every line here traces to a customer-side production record, not a vendor-reported figure.
Every enterprise buyer evaluates three risks before deploying AI in hiring, implementation, adoption, accuracy. The carrier's own evidence answers all three. After that, the system gets sharper with every hire: success profiles retrain on customer outcomes, inside the customer VPC.
A 30-minute architecture walk with your compliance team. We bring the data-flow diagram, a sample audit trail, and the on-job correlation pack, printed, redlined against your requirements.
What your team leaves with