Data in. Intelligence out. Outcomes back in.

Upload CSV files for students, courses, enrollments, grades and attendance. Map columns, preview, validate, then confirm. Duplicates and errors are reported.

Run analysis to build student profiles and detect knowledge gaps from the evidence.

Risk signals appear where evidence supports them, with factors and confidence.

Review proposed interventions, remediation and learning paths. Staff approve; agents prepare the work.

Record outcomes with before and after metrics. They inform later recommendations.

AI operations consume organization credits, visible on the usage page.