98% of your top performers would never have been interviewed.
We tested every screening filter a Fortune 500 insurance carrier runs — keywords, industry experience, assessments — against 10,765 hires and four years of actual production data. The filters don't predict. We built the infrastructure that does.
Keyword filters don't predict production. Ours do.
We ran 8,181 candidate keywords against four years of post-hire production at a Fortune 500 carrier. After Bonferroni correction, none predicted sustained performance. Thirty were anti-predictive — correlated with lower output.
The standard ATS funnel at this customer eliminated 98% of their eventual top performers cumulatively. The industry-experience filter alone eliminated 80% of them.
Source: 4-year retrospective on 10,765 agents at a Fortune 500 carrier. Top performers defined as sustained-production cohort (p75+ over 18 months).
Every hire, from filter to field, as queryable evidence.
A Decision Trace connects what the ATS screened on, what the assessment measured, what the interview surfaced, and what actually happened in production — for every candidate, indefinitely.
Institutional knowledge becomes auditable. When a hiring manager leaves, their judgment doesn't leave with them.
- 01 · IngestEvery candidate event from your ATS, HRIS, assessments, and CRM zero data egress
- 02 · AttributeEvery decision — screen, advance, reject, hire — linked to its originating signal reversible
- 03 · MeasureEach signal scored against real production outcomes, not interviewer gut feel compound over time
- 04 · ReplayQuery "show me every hire where the industry filter would have rejected a top performer" auditable
One model. The entire employee lifecycle.
Scoring logic calibrated against your validated top performers flows through every stage — sourcing, screening, interviewing, ramping, retaining, promoting. The model compounds.
A 0–100 score, written back to your ATS as a native field.
Calibrated per role and per location against your validated top-performer pattern. 28+ behavioral, skill, and cultural dimensions. Every score ships with a plain-English rationale.
Structured interviews conducted mid-funnel.
Scores auto-update against production outcomes — not interviewer gut feel. Full transcripts, signal timestamps, and signed audit trails available to hiring managers.
Find passive candidates that match what actually predicts production.
Identifies passive candidates across LinkedIn, GitHub, public portfolios, and industry networks using the same patterns the model learned from your own wins — not inferred boilerplate.
Ten agents, one model, a compounding scoring substrate.
The same model that screens your pipeline runs ramp acceleration, retention risk, attrition modeling, internal mobility, succession planning, manager intelligence, and career pathing.
Inside your VPC. Zero data egress. Legal in weeks.
Single-tenant deployment in customer-owned infrastructure. Fine-tuned, open-source models — no third-party AI in the chain. SOC 2 Type II. Reviewable by your security team the same way Snowflake is.
See what the last four years of your hires actually predicted.
We'll run our backtest against a sample of your production data, in your environment. You'll see — numerically — which of your filters worked, which didn't, and what a calibrated Fit Score would have done differently.