Campus AI workflow
ProductionMatches incoming transcripts to student records, scores its own confidence, and sends the uncertain ones to staff.
- 60,000Annual workflow volumeApproximate transcripts per year
- 225/hrProcessing throughputReported workflow rate
- 99.86%Published accuracy3,700+ records; five reported errors
Accountability
How this use case is governed
- Service owner
- TritonAI solutions team and the sponsoring student-services owner
- Human oversight
- Staff review uncertain matches and monitor quality before any broader use.
- Measurement plan
- Match precision and recall, review volume, processing time, and exception rate.
- Data boundary
- Public description; student records require approved protected-data controls
Product media
See the workflow in action
Silent screen recording showing confidence-based matching and the staff review interface used to resolve uncertain records.
- OCR Extraction
- Confidence Scoring
- Human Review Queue
How it works
How the workflow fits
The problem it addresses, what it actually does, and how far along it is.
Problem
Transcripts arrive in large volumes with inconsistent layouts, identifiers, and scan quality. Matching them by hand is slow, and getting one wrong has real consequences for a student.
Solution
The workflow extracts candidate identifiers, compares the evidence, and assigns confidence so staff can focus on exceptions. It runs in production with continuous monitoring and human review of uncertain matches.
Current status
In production for UC San Diego enrollment management. The workflow ingests incoming transcripts, extracts candidate identifiers via OCR, compares each transcript with the appropriate student record, assigns confidence scores, and routes uncertain matches to staff for review. The published validation result reported 99.86% accuracy across more than 3,700 records, with five errors; staff review remains part of the service.