During DMIT analysis, we integrate minutiae distribution, triradius geometry of fingerprint, and ridge-count metrics with psychometric constructs to generate a nuanced learner profile. Bayesian-weighted cues and cross-domain covariance help prioritize interventions across attention, memory encoding, and problem-solving. You’ll receive plain-language insights, plus a structured regimen—micro-goals, spaced-repetition schedules, and feedback loops—aligned to your context. This is an advisory, hypothesis-driven model, not a medical or IQ test; we calibrate recommendations continually using observable outcomes and parent-mentor reflections and periodic progress audits.