An AI-assisted academic decision intelligence platform that parses student transcripts and university policies to recommend optimal course workloads, manage graduation risks, and guide career paths.
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Coursa was designed as a prototype for Indonesian university students planning their academic path. It helps students understand study-plan credit limits, prerequisite rules, graduation readiness, thesis eligibility, and career direction from their own academic records.
Students often need to manually review transcripts and curriculum rules before registering for semester courses. This can lead to prerequisite mistakes, wrong credit-load planning, missed failed core courses, and poorly aligned thesis or career decisions.
Coursa parses uploaded transcripts and university policy documents into structured data. The app stores that data in PostgreSQL and sends compact profile summaries to the advisor chat, combining deterministic academic rules with natural language guidance.
The platform separates high-token document ingestion from fast conversational advising. Multi-modal AI extracts transcript and policy data once, while a TypeScript rule engine calculates credit limits, academic risk, graduation readiness, and thesis eligibility before recommendations are shown.
Developed the dual-stage AI processing pipeline, built the TypeScript academic rule engine, implemented the student dashboard and advisor interface, and designed database schemas for secure sessions, student profiles, and snapshot history tracking.
Structured profile summaries reduce per-chat token usage by roughly 90% compared with resending raw documents. Automated academic rule checks turn manual curriculum verification from hours into seconds, and the containerized deployment is ready for serverless production environments.
We can help turn document-heavy workflows into structured data, reliable rules, and AI-assisted decision support.
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