Different teachers record progress differently. There is no common readiness standard across classrooms.
By the time regression is noticed, months have passed. The window for early support closes silently.
Individual Education Plans often become templates, not truly individualized roadmaps for each child.
Vocational matching is based on assumption, not measurable readiness data tied to real capabilities.
Mathematical scoring ensures consistency. AI adds contextual recommendations. Every decision is transparent — no black boxes.
ARIE flags decline weeks before it becomes critical, giving teachers time to intervene when it actually matters.
Not just scores — clear, specific next steps for each teen. Teachers know exactly what to do on Monday morning.
Job pathways aligned to measurable capability, not guesswork. Every recommendation is grounded in real readiness data.
ARIE combines a deterministic readiness scoring engine with an AI-powered recommendation layer. Built using FastAPI + PostgreSQL + scalable vector computation, designed for institutional deployment across India.
A teacher writes:
“Needed two reminders to complete packaging task, lost focus after 15 minutes, improved after encouragement.”
ARIE converts this into structured readiness data and recommends:
Every neurodiverse teen deserves a measurable, structured pathway from education to employment. ARIE aims to become the backbone system that enables that transition at scale.