Placement Prediction

It forecasts cohort employability and readiness months before placement season begins, shifting your institution from reactive tracking to proactive placement strategy.

The engine aggregates multi-dimensional data across the student lifecycle: Academic Attainment (real-time CGPA, core-subject performance, and historical progression), Skill Graph Alignment (demonstrated skills from projects and labs cross-referenced against current market demand APIs), and Behavioral Readiness (mock interview performance, communication aptitude scores, and participation metrics).

It generates a highly accurate probability score for final placement for each student, built from academic, skill, and behavioral signals.

It compares your cohort's real-time readiness against the exact hiring requirements of your top recruitment partners. For example, if Microsoft requires strong Data Structures and your 3rd-year CS cohort is trending low in algorithms, you have six months to deploy an intervention.

The proactive roadmap runs in four steps: ingest historical placement data, map current cohort skills, identify market deficits, and deploy targeted bootcamps.