Academic Transcript Ingestion System

A regional university system processed thousands of academic transcripts every admissions cycle. Each transcript came in a different format — PDFs from other institutions, scanned documents, digital records with inconsistent structures. The admissions team was spending weeks on manual data entry and cross-referencing.

The business cost was significant: slower admissions decisions meant lost students to competing institutions. Transfer credit evaluations that should take hours were taking days. And the error rate on manual entry was introducing downstream problems in student records.

Previous attempts at automation had failed because off-the-shelf OCR tools couldn't handle the variety of transcript formats, and rule-based parsers broke every time a new institution's format appeared.

We built a multi-stage document intelligence pipeline that could handle any transcript format — scanned, digital, or hybrid. The system combines advanced OCR with LLM-based extraction to understand document structure, not just read text.

The architecture was designed around three key decisions: a layout-aware document model that understands tables, headers, and hierarchical relationships; a normalization layer mapping any institution's grading scale to the university's internal standard; and a confidence-scoring system that flags low-certainty extractions for human review.

Integration with the university's existing student information system was non-negotiable. We engineered the pipeline to output directly into their SIS format, so the admissions team's workflow didn't change — the data just appeared faster and cleaner.