TransferTrak [demo]

AI-powered academic transcript evaluation engine sponsored by BeaconForge Solutions. Automates the tedious, error-prone process of parsing registrar transcripts and finding transfer course equivalencies across institutional catalogs.

Gemini reads the PDF into structured courses; BM25 (and optionally embeddings) narrows the catalog to a shortlist per line; Gemini only adjudicates within that shortlist—fast, grounded, and suitable for review workflows.

1. Multimodal Extraction

Ingests complex registrar PDFs directly using Gemini vision and structured JSON schema output. Extracts institution header metadata (issuing school, degree program, GPA, dates) and normalizes every completed course into structured records containing course codes, titles, credits, and grades.

2. Catalog Shortlisting (BM25 + Dense Retrieval)

Instead of dumping massive institutional course catalogs into LLM context windows, TransferTrak indexes target catalogs using BM25 lexical ranking alongside dense embeddings. For each extracted course, the retrieval layer selects a top-K candidate shortlist in single-digit milliseconds.

3. Grounded Adjudication

The LLM adjudicator only evaluates candidates within the retrieved shortlist. It maps equivalencies, computes confidence scores, or flags edge cases for human review with specific, auditable reasoning (e.g. missing prerequisite components or credit discrepancies).

• Problem: Transfer admissions staff manually re-key transcripts and hunt catalog equivalents line by line. Review queues back up, processing takes weeks, and institutional consistency suffers.
• Upload: Drop a representative transcript (e.g. transcripts/nku.pdf or transcripts/trc.pdf), select the destination catalog, and run Extract & match with refinement enabled.
• Extraction: Review the extracted student and issuing school metadata alongside the standardized course list produced in Step 1.
• Matches: Inspect a complex or non-standard row to view the BM25 retrieval shortlist table side-by-side with the refinement card (confidence score, mapped course, or human review tag + explanatory reason).
• Close: Review the summary results strip (latency timings, total course count, high/medium/human review distribution) and highlight the open /docs API for SIS and CRM integration.