HyperPersonal is built on the IPIP-300 item framework, measuring five broad personality domains and thirty underlying facets. Results are continuous dimensions, not fixed personality categories.
A four-letter type discards most of the information in an answer. A facet-level profile keeps it: two people with the same broad score can differ sharply underneath, and it is usually the difference that matters at work.
Scores are percentiles against a reference population, not scores out of 100 and not types. Someone near the middle of a domain is described as near the middle, not pushed to the nearer edge.
MBTI gives teams a shared vocabulary for difference, and that is a real use. The objection is to the measurement step: the traits underneath a type sort are continuously distributed and most people sit near the middle, so cutting at a midpoint puts two people a fraction apart into opposite categories and reports someone at the extreme and someone barely past the line as the same letter. Because so many people sit close to the cut, ordinary measurement error is enough to move them across it, and retest studies have repeatedly found a substantial share receive a different type weeks later. MBTI also carries no emotional-stability dimension, which in the Five-Factor literature is among the domains most consistently associated with how people respond to pressure. If your organisation already runs MBTI the two are not in competition: keep it as shared language, and use continuous facet data where a decision is being made.
The IPIP-300 item pool and the Big Five model are public-domain and research-based. HyperPersonal’s software, AI interpretation layer, workflow tools and organisational applications are developed by HyperPersonal AI, and inherit no validation from the item pool.
Six protocol-validity indices in three families are computed from item-level responses. Someone presenting a flattering profile answers more consistently than average, so these indices move in their favour — they are not a lie detector, and scores are never adjusted.
We are conducting ongoing product and outcome validation with organisational partners. We do not claim predictive validity for job performance and will not until we have measured it.
Operated by Alano Tech Pte. Ltd., Singapore.
Also from us: Alano.ai Opptymizer Omu Labs