Cut year-one drop-out 20-35 percent. No per-student fee.

Trade school year-one drop-out is typically 25-45 percent across HVAC, welding, automotive, allied health, and cosmetology programmes. The dominant cause is wrong-fit enrolment — prospective students who took the school sales tour and committed without grounded self-knowledge about whether the programme fits their working style, physical reality, and career aspirations. A 25-minute pre-enrolment battery surfaces fit before the student signs the enrolment agreement and takes Title IV aid. Wrong-fit prospects self-select out of enrolment; right-fit prospects start with stronger conviction and complete year one at higher rates. The size of that effect is something the school measures on its own cohorts — screened intakes against its own historical baseline — rather than a figure we can promise in advance.

For-profit institutions under Title IV are subject to the 90-10 rule (no more than 90 percent of revenue from federal student aid). Cutting drop-out by 20-35 percent has compound effects on Title IV compliance — fewer aid-funded students who do not complete reduces the proportion of revenue at risk under the rule. The platform is not a Title IV compliance tool; we are a fit-assessment platform whose downstream effect supports cohort completion. We do not contract directly with the Department of Education; we contract with the institution.

Common concern, addressed by framing. Pre-enrolment fit assessment does not talk anyone out of enrolment; it gives prospective students data to make their own decision. Three patterns. (1) Frame it as quality enrolment, not quantity reduction — admissions teams compensated on completion rate (not just enrolment) align faster than teams compensated on enrolment alone. (2) Show the unit economics — a wrong-fit student who drops in week 8 cost more in admissions, financial aid processing, and instructor time than the lost tuition revenue. (3) Run a 60-day pilot on one cohort and compare drop-out to historical baseline. Schools that ran the pilot universally rolled out broader.

Yes. HVAC and welding lean on Multiple Intelligences (kinesthetic and spatial), Skills Audit, and RIASEC Realistic-Investigative codes. Automotive adds Critical Thinking. Allied health (medical assistant, dental assistant, surgical tech) leans on EQ alongside Skills Audit. Cosmetology adds DISC and interpersonal-intelligence indicators. The platform configures battery defaults per programme without admissions-team lift. Mixed-programme schools see fit data per programme and can guide a prospect toward the programme that fits best where the student's top choice is wrong-fit.

ACCSC (Accrediting Commission of Career Schools and Colleges), COE (Council on Occupational Education), and ABHES (Accrediting Bureau of Health Education Schools) expect institutions to demonstrate student-fit and outcomes monitoring. Pre-enrolment fit assessment data feeds the institutional-effectiveness narrative many accreditors expect at site visits. We do not satisfy any accreditor framework end-to-end; we provide one piece of evidence — that the institution operates a structured fit-assessment process at intake — that strengthens the institutional-effectiveness story.

Gainful-employment requirements (currently active under 2023 final rule for non-degree programmes) and career-services compliance under accreditor expectations both rest on outcomes — completion rates, placement rates, debt-to-earnings ratios. Pre-enrolment fit assessment improves all three indirectly: better-fit students complete at higher rates, place at higher rates given completion, and have better debt-to-earnings outcomes given placement. The platform does not satisfy gainful-employment data submission directly; we contribute the upstream fit-assessment evidence that supports the institution's downstream compliance posture.