InzightEd
AI-Driven Performance Intelligence for NEET & JEE Institutes
AI-driven performance intelligence platform for NEET & JEE institutes. Processes parallel OMR batches, runs graph traversal across 100+ longitudinal tests per student, and synthesizes AI-generated improvement plans — all at production scale on AWS.
The Challenge
Institutes were drowning in OMR test data with no intelligent way to correlate mistakes across subjects, identify conceptual gaps over time, or deliver personalized improvement plans at scale. Manual analysis couldn't keep up with hundreds of students each sitting 100+ tests.
Our Solution
We built a multi-stage pipeline: parallel OCR ingestion → Neo4j graph correlation → time-series PostgreSQL analytics → batched Gemini AI synthesis. The result is a platform that turns raw scan data into actionable student insights in seconds.
Product Screenshots
Home — InzightEd Platform
Technical Highlights
OMR sheets are extracted concurrently across student batches — eliminating serial bottlenecks and cutting ingestion time proportionally to batch size.
Neo4j traversal algorithms surface cross-subject mistake patterns in a single graph walk — no N+1 relational joins, constant-time correlation at any test volume.
Time-series indexed PostgreSQL queries return longitudinal trends across 100+ tests in sub-second response times, even as data volume grows.
Stateless Django workers behind an AWS load balancer scale linearly with institute size — no shared in-process state between request handlers.
Gemini API calls are batched per student cohort and response-cached, reducing per-student inference latency and controlling cost at scale.
Raw scan → OCR extraction → validation → graph ingestion → AI synthesis. Each stage is independently scalable and restartable on failure.
System Architecture
End-to-end system architecture — InzightEd on AWS
Tech Stack
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