AI Enrollment Automation - From Records to Submitted Application
AI that reads applicant records and transcripts, assembles enrollment applications, and submits them to the program.
The Opportunity
The client is an education services organization that enrolls applicants into credentialing, certification, and placement programs. Eligibility is established in the applicant's academic and professional record, and acceptance hinges on getting the paperwork right. Each application required staff to gather transcripts, certifications, and supporting documents, extract the right details, and assemble everything into the program's required application format before submitting it. The process was manual at every step, and the time spent per application scaled accordingly. It was slow, it consumed staff who had better things to do, and it produced errors (missed fields and mismatched documents) that sent applications back for correction. For the applicants on the other side, every one of those delays was time spent waiting on paperwork while their placement sat idle.
The paperwork was rule-bound, repetitive, and document-driven. Every application drew on the same categories of source material, transcripts and applicant records, and produced the same output, a complete application in the program's required format. The opportunity was to automate the full span: read the inbound documents, extract what the application requires, assemble the final submission, and deliver it to the program, with FERPA-aligned handling of student information throughout.
Key Challenges
Staff pulled details from transcripts, certifications, and records and re-keyed them into each application by hand.
One missed field or mismatched document sent the application back for correction and restarted the applicant's wait.
Packets piled up at intake deadlines, so applicants who had already cleared eligibility waited on paperwork to complete enrollment.
Every additional cohort meant more manual processing, with no way to scale enrollment except adding staff.
The Process
Mapped the enrollment flow from the first inbound document through final application and submission, cataloging every source document, every required field, and every step where manual handling introduced delay or error.
Built the automation to read the transcripts, certifications, and records the program draws on, extracting the details the application requires from each one. Handling of student information is FERPA-aligned throughout.
Extracted data is assembled into the program's required application format, complete and internally consistent. The form-filling and cross-checking that consumed staff time now happens automatically, the same way every time.
Finished applications are submitted to the education program as part of the same flow. The pipeline was refined against real enrollment cases, so edge cases surfaced and were handled before staff stepped away from the manual process.
Our Solution
Ajaia built an enrollment automation that turns applicant documents into a submitted program application. AI reads transcripts, certifications, and applicant records and extracts the details the application requires, with FERPA-aligned handling. Extracted data is assembled into the final application, complete and consistent, with manual effort limited to exceptions and review. Finished applications go to the education program as part of the same flow, closing the loop from documents to submission.
Key Capabilities
Impact
Transcripts, certifications, and applicant records flow into a complete, consistent application with only 5% manual effort required for exceptions and review, and submission to the program happens in the same pass. Applications go out faster and cleaner, the correction-and-resubmission loop has narrowed to cases that require human judgment, and the intake-season pile-up no longer sets the pace of an applicant's wait. Staff work on applicants instead of paperwork, and the organization can grow enrollment without adding processing labor behind it.
Key Results
- 98% of applications auto-processed end to end
- 95% reduction in enrollment processing time
- 5% manual effort required across the enrollment pipeline
- Error-and-resubmission loop narrowed to cases requiring human judgment
- Enrollment volume can grow without proportional growth in processing staff
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